Overview

Dataset statistics

Number of variables35
Number of observations314288
Missing cells3229562
Missing cells (%)29.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.9 MiB
Average record size in memory280.0 B

Variable types

Categorical21
Numeric12
Boolean1
Unsupported1

Alerts

FRACT OWNER has constant value "True" Constant
N-NUMBER has a high cardinality: 314288 distinct values High cardinality
SERIAL NUMBER has a high cardinality: 245285 distinct values High cardinality
MFR MDL CODE has a high cardinality: 45855 distinct values High cardinality
NAME has a high cardinality: 213600 distinct values High cardinality
STREET has a high cardinality: 188558 distinct values High cardinality
STREET2 has a high cardinality: 3818 distinct values High cardinality
CITY has a high cardinality: 15202 distinct values High cardinality
STATE has a high cardinality: 59 distinct values High cardinality
ZIP CODE has a high cardinality: 180646 distinct values High cardinality
COUNTRY has a high cardinality: 85 distinct values High cardinality
CERTIFICATION has a high cardinality: 305 distinct values High cardinality
OTHER NAMES(1) has a high cardinality: 32698 distinct values High cardinality
OTHER NAMES(2) has a high cardinality: 3180 distinct values High cardinality
OTHER NAMES(3) has a high cardinality: 1067 distinct values High cardinality
OTHER NAMES(4) has a high cardinality: 568 distinct values High cardinality
OTHER NAMES(5) has a high cardinality: 461 distinct values High cardinality
KIT MFR has a high cardinality: 481 distinct values High cardinality
KIT MODEL has a high cardinality: 956 distinct values High cardinality
MODE S CODE HEX has a high cardinality: 314288 distinct values High cardinality
YEAR MFR is highly correlated with AIR WORTH DATEHigh correlation
LAST ACTION DATE is highly correlated with EXPIRATION DATEHigh correlation
TYPE AIRCRAFT is highly correlated with TYPE ENGINEHigh correlation
TYPE ENGINE is highly correlated with TYPE AIRCRAFTHigh correlation
AIR WORTH DATE is highly correlated with YEAR MFRHigh correlation
EXPIRATION DATE is highly correlated with LAST ACTION DATEHigh correlation
LAST ACTION DATE is highly correlated with EXPIRATION DATEHigh correlation
EXPIRATION DATE is highly correlated with LAST ACTION DATEHigh correlation
YEAR MFR is highly correlated with AIR WORTH DATEHigh correlation
LAST ACTION DATE is highly correlated with EXPIRATION DATEHigh correlation
TYPE AIRCRAFT is highly correlated with TYPE ENGINEHigh correlation
TYPE ENGINE is highly correlated with TYPE AIRCRAFTHigh correlation
AIR WORTH DATE is highly correlated with YEAR MFRHigh correlation
EXPIRATION DATE is highly correlated with LAST ACTION DATEHigh correlation
STATUS CODE is highly correlated with FRACT OWNERHigh correlation
FRACT OWNER is highly correlated with STATUS CODE and 3 other fieldsHigh correlation
REGION is highly correlated with FRACT OWNER and 1 other fieldsHigh correlation
COUNTRY is highly correlated with FRACT OWNER and 1 other fieldsHigh correlation
STATE is highly correlated with FRACT OWNER and 2 other fieldsHigh correlation
ENG MFR MDL is highly correlated with TYPE AIRCRAFT and 2 other fieldsHigh correlation
STATE is highly correlated with REGION and 2 other fieldsHigh correlation
REGION is highly correlated with STATE and 2 other fieldsHigh correlation
COUNTY is highly correlated with STATE and 1 other fieldsHigh correlation
COUNTRY is highly correlated with STATE and 1 other fieldsHigh correlation
LAST ACTION DATE is highly correlated with CERT ISSUE DATE and 2 other fieldsHigh correlation
CERT ISSUE DATE is highly correlated with LAST ACTION DATEHigh correlation
TYPE AIRCRAFT is highly correlated with ENG MFR MDL and 1 other fieldsHigh correlation
TYPE ENGINE is highly correlated with ENG MFR MDL and 3 other fieldsHigh correlation
STATUS CODE is highly correlated with LAST ACTION DATE and 1 other fieldsHigh correlation
AIR WORTH DATE is highly correlated with ENG MFR MDL and 2 other fieldsHigh correlation
EXPIRATION DATE is highly correlated with LAST ACTION DATE and 1 other fieldsHigh correlation
UNIQUE ID is highly correlated with TYPE ENGINE and 1 other fieldsHigh correlation
ENG MFR MDL has 29923 (9.5%) missing values Missing
YEAR MFR has 41228 (13.1%) missing values Missing
STREET2 has 301400 (95.9%) missing values Missing
CERT ISSUE DATE has 12427 (4.0%) missing values Missing
CERTIFICATION has 27905 (8.9%) missing values Missing
FRACT OWNER has 313488 (99.7%) missing values Missing
AIR WORTH DATE has 40839 (13.0%) missing values Missing
OTHER NAMES(1) has 276202 (87.9%) missing values Missing
OTHER NAMES(2) has 310686 (98.9%) missing values Missing
OTHER NAMES(3) has 313173 (99.6%) missing values Missing
OTHER NAMES(4) has 313690 (99.8%) missing values Missing
OTHER NAMES(5) has 313805 (99.8%) missing values Missing
EXPIRATION DATE has 14297 (4.5%) missing values Missing
KIT MFR has 300388 (95.6%) missing values Missing
KIT MODEL has 300388 (95.6%) missing values Missing
X35 has 314288 (100.0%) missing values Missing
YEAR MFR is highly skewed (γ1 = -29.06525491) Skewed
N-NUMBER is uniformly distributed Uniform
OTHER NAMES(3) is uniformly distributed Uniform
OTHER NAMES(4) is uniformly distributed Uniform
OTHER NAMES(5) is uniformly distributed Uniform
MODE S CODE HEX is uniformly distributed Uniform
N-NUMBER has unique values Unique
MODE S CODE has unique values Unique
UNIQUE ID has unique values Unique
MODE S CODE HEX has unique values Unique
X35 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ENG MFR MDL has 8790 (2.8%) zeros Zeros
TYPE ENGINE has 10111 (3.2%) zeros Zeros

Reproduction

Analysis started2022-06-08 03:24:03.534944
Analysis finished2022-06-08 03:25:37.960097
Duration1 minute and 34.43 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

N-NUMBER
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct314288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
 
1
68576
 
1
6857P
 
1
6857M
 
1
6857J
 
1
Other values (314283)
314283 

Length

Max length5
Median length5
Mean length4.839192079
Min length1

Characters and Unicode

Total characters1520900
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314288 ?
Unique (%)100.0%

Sample

1st row1
2nd row100
3rd row10001
4th row10002
5th row10003

Common Values

ValueCountFrequency (%)
11
 
< 0.1%
685761
 
< 0.1%
6857P1
 
< 0.1%
6857M1
 
< 0.1%
6857J1
 
< 0.1%
6857H1
 
< 0.1%
6857G1
 
< 0.1%
6857D1
 
< 0.1%
685791
 
< 0.1%
685751
 
< 0.1%
Other values (314278)314278
> 99.9%

Length

2022-06-07T23:25:38.158224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11
 
< 0.1%
1000f1
 
< 0.1%
100041
 
< 0.1%
100061
 
< 0.1%
100071
 
< 0.1%
100081
 
< 0.1%
100091
 
< 0.1%
1000a1
 
< 0.1%
1000e1
 
< 0.1%
1000h1
 
< 0.1%
Other values (314278)314278
> 99.9%

Most occurring characters

ValueCountFrequency (%)
1131987
 
8.7%
2125505
 
8.3%
5115972
 
7.6%
3115866
 
7.6%
4113793
 
7.5%
7113247
 
7.4%
6108399
 
7.1%
8107261
 
7.1%
9105834
 
7.0%
093044
 
6.1%
Other values (24)389992
25.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1130908
74.4%
Uppercase Letter389992
 
25.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A25410
 
6.5%
C24267
 
6.2%
S23478
 
6.0%
M21585
 
5.5%
D20753
 
5.3%
B20611
 
5.3%
T19939
 
5.1%
P19697
 
5.1%
R18969
 
4.9%
W18273
 
4.7%
Other values (14)177010
45.4%
Decimal Number
ValueCountFrequency (%)
1131987
11.7%
2125505
11.1%
5115972
10.3%
3115866
10.2%
4113793
10.1%
7113247
10.0%
6108399
9.6%
8107261
9.5%
9105834
9.4%
093044
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common1130908
74.4%
Latin389992
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A25410
 
6.5%
C24267
 
6.2%
S23478
 
6.0%
M21585
 
5.5%
D20753
 
5.3%
B20611
 
5.3%
T19939
 
5.1%
P19697
 
5.1%
R18969
 
4.9%
W18273
 
4.7%
Other values (14)177010
45.4%
Common
ValueCountFrequency (%)
1131987
11.7%
2125505
11.1%
5115972
10.3%
3115866
10.2%
4113793
10.1%
7113247
10.0%
6108399
9.6%
8107261
9.5%
9105834
9.4%
093044
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1520900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1131987
 
8.7%
2125505
 
8.3%
5115972
 
7.6%
3115866
 
7.6%
4113793
 
7.5%
7113247
 
7.4%
6108399
 
7.1%
8107261
 
7.1%
9105834
 
7.0%
093044
 
6.1%
Other values (24)389992
25.6%

SERIAL NUMBER
Categorical

HIGH CARDINALITY

Distinct245285
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
001
 
1949
1
 
1196
002
 
257
0001
 
203
01
 
199
Other values (245280)
310484 

Length

Max length30
Median length27
Mean length6.490620068
Min length1

Characters and Unicode

Total characters2039924
Distinct characters50
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique228203 ?
Unique (%)72.6%

Sample

1st row1071
2nd row5334
3rd rowA28
4th row79-030
5th row1

Common Values

ValueCountFrequency (%)
0011949
 
0.6%
11196
 
0.4%
002257
 
0.1%
0001203
 
0.1%
01199
 
0.1%
101184
 
0.1%
2171
 
0.1%
1001158
 
0.1%
003143
 
< 0.1%
3110
 
< 0.1%
Other values (245275)309718
98.5%

Length

2022-06-07T23:25:38.351057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0012002
 
0.6%
11249
 
0.4%
002277
 
0.1%
0001276
 
0.1%
01216
 
0.1%
101198
 
0.1%
2188
 
0.1%
1001163
 
0.1%
003151
 
< 0.1%
3124
 
< 0.1%
Other values (244695)312282
98.5%

Most occurring characters

ValueCountFrequency (%)
1266951
13.1%
0251822
12.3%
2244289
12.0%
5163124
8.0%
7163054
8.0%
8152837
7.5%
3148072
7.3%
4137399
6.7%
6135188
6.6%
-115861
5.7%
Other values (40)261327
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1763757
86.5%
Uppercase Letter155002
 
7.6%
Dash Punctuation115861
 
5.7%
Space Separator2839
 
0.1%
Other Punctuation2366
 
0.1%
Open Punctuation37
 
< 0.1%
Close Punctuation37
 
< 0.1%
Connector Punctuation12
 
< 0.1%
Math Symbol12
 
< 0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A22987
14.8%
C20468
13.2%
R12546
 
8.1%
D12186
 
7.9%
B10645
 
6.9%
T9254
 
6.0%
E8625
 
5.6%
S8526
 
5.5%
P5962
 
3.8%
H5365
 
3.5%
Other values (16)38438
24.8%
Decimal Number
ValueCountFrequency (%)
1266951
15.1%
0251822
14.3%
2244289
13.9%
5163124
9.2%
7163054
9.2%
8152837
8.7%
3148072
8.4%
4137399
7.8%
6135188
7.7%
9101021
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.1714
72.4%
/605
 
25.6%
#27
 
1.1%
:17
 
0.7%
&2
 
0.1%
'1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
=9
75.0%
+3
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
-115861
100.0%
Space Separator
ValueCountFrequency (%)
2839
100.0%
Open Punctuation
ValueCountFrequency (%)
(37
100.0%
Close Punctuation
ValueCountFrequency (%)
)37
100.0%
Connector Punctuation
ValueCountFrequency (%)
_12
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1884922
92.4%
Latin155002
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A22987
14.8%
C20468
13.2%
R12546
 
8.1%
D12186
 
7.9%
B10645
 
6.9%
T9254
 
6.0%
E8625
 
5.6%
S8526
 
5.5%
P5962
 
3.8%
H5365
 
3.5%
Other values (16)38438
24.8%
Common
ValueCountFrequency (%)
1266951
14.2%
0251822
13.4%
2244289
13.0%
5163124
8.7%
7163054
8.7%
8152837
8.1%
3148072
7.9%
4137399
7.3%
6135188
7.2%
-115861
6.1%
Other values (14)106325
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2039924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1266951
13.1%
0251822
12.3%
2244289
12.0%
5163124
8.0%
7163054
8.0%
8152837
7.5%
3148072
7.3%
4137399
6.7%
6135188
6.6%
-115861
5.7%
Other values (40)261327
12.8%

MFR MDL CODE
Categorical

HIGH CARDINALITY

Distinct45855
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
7102802
 
4753
2072418
 
3843
7102808
 
3735
7100510
 
3723
2072434
 
3396
Other values (45850)
294838 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2200016
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39867 ?
Unique (%)12.7%

Sample

1st row3980115
2nd row7100510
3rd row9601202
4th row8930105
5th row056336T

Common Values

ValueCountFrequency (%)
71028024753
 
1.5%
20724183843
 
1.2%
71028083735
 
1.2%
71005103723
 
1.2%
20724343396
 
1.1%
21300013283
 
1.0%
71028072353
 
0.7%
21101022307
 
0.7%
20758162181
 
0.7%
71018282094
 
0.7%
Other values (45845)282620
89.9%

Length

2022-06-07T23:25:38.483385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
71028024753
 
1.5%
20724183843
 
1.2%
71028083735
 
1.2%
71005103723
 
1.2%
20724343396
 
1.1%
21300013283
 
1.0%
71028072353
 
0.7%
21101022307
 
0.7%
20758162181
 
0.7%
71018282094
 
0.7%
Other values (45845)282620
89.9%

Most occurring characters

ValueCountFrequency (%)
0527143
24.0%
2334531
15.2%
1332521
15.1%
7199118
 
9.1%
5143963
 
6.5%
3136750
 
6.2%
6131104
 
6.0%
8130324
 
5.9%
4126813
 
5.8%
958244
 
2.6%
Other values (26)79505
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2120511
96.4%
Uppercase Letter79505
 
3.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C5075
 
6.4%
N4596
 
5.8%
I3827
 
4.8%
A3765
 
4.7%
B3686
 
4.6%
P3202
 
4.0%
K3116
 
3.9%
Y3074
 
3.9%
L2983
 
3.8%
D2909
 
3.7%
Other values (16)43272
54.4%
Decimal Number
ValueCountFrequency (%)
0527143
24.9%
2334531
15.8%
1332521
15.7%
7199118
 
9.4%
5143963
 
6.8%
3136750
 
6.4%
6131104
 
6.2%
8130324
 
6.1%
4126813
 
6.0%
958244
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common2120511
96.4%
Latin79505
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C5075
 
6.4%
N4596
 
5.8%
I3827
 
4.8%
A3765
 
4.7%
B3686
 
4.6%
P3202
 
4.0%
K3116
 
3.9%
Y3074
 
3.9%
L2983
 
3.8%
D2909
 
3.7%
Other values (16)43272
54.4%
Common
ValueCountFrequency (%)
0527143
24.9%
2334531
15.8%
1332521
15.7%
7199118
 
9.4%
5143963
 
6.8%
3136750
 
6.4%
6131104
 
6.2%
8130324
 
6.1%
4126813
 
6.0%
958244
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2200016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0527143
24.0%
2334531
15.2%
1332521
15.1%
7199118
 
9.1%
5143963
 
6.5%
3136750
 
6.2%
6131104
 
6.0%
8130324
 
5.9%
4126813
 
5.8%
958244
 
2.6%
Other values (26)79505
 
3.6%

ENG MFR MDL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2157
Distinct (%)0.8%
Missing29923
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean33744.96211
Minimum0
Maximum99999
Zeros8790
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:38.614657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9050
Q117026
median41506
Q341533
95-th percentile59302
Maximum99999
Range99999
Interquartile range (IQR)24507

Descriptive statistics

Standard deviation19176.03281
Coefficient of variation (CV)0.568263575
Kurtosis1.683481676
Mean33744.96211
Median Absolute Deviation (MAD)14056
Skewness0.8362442128
Sum9595886150
Variance367720234.2
MonotonicityNot monotonic
2022-06-07T23:25:38.752892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4150830135
 
9.6%
1702613663
 
4.3%
4151413257
 
4.2%
1703210540
 
3.4%
1702010233
 
3.3%
1700310040
 
3.2%
08790
 
2.8%
999997397
 
2.4%
415057382
 
2.3%
170227261
 
2.3%
Other values (2147)165667
52.7%
(Missing)29923
 
9.5%
ValueCountFrequency (%)
08790
2.8%
40115
 
< 0.1%
4028
 
< 0.1%
50016
 
< 0.1%
5011
 
< 0.1%
51110
 
< 0.1%
5501
 
< 0.1%
10017
 
< 0.1%
100269
 
< 0.1%
10071
 
< 0.1%
ValueCountFrequency (%)
999997397
2.4%
833592
 
< 0.1%
833587
 
< 0.1%
8000023
 
< 0.1%
7500113
 
< 0.1%
7200036
 
< 0.1%
700043
 
< 0.1%
672762
 
< 0.1%
672701
 
< 0.1%
672632
 
< 0.1%

YEAR MFR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct110
Distinct (%)< 0.1%
Missing41228
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean1976.838274
Minimum0
Maximum2017
Zeros208
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:38.892265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1946
Q11964
median1976
Q31999
95-th percentile2013
Maximum2017
Range2017
Interquartile range (IQR)35

Descriptive statistics

Standard deviation59.05198883
Coefficient of variation (CV)0.02987193723
Kurtosis968.8110114
Mean1976.838274
Median Absolute Deviation (MAD)16
Skewness-29.06525491
Sum539795459
Variance3487.137384
MonotonicityNot monotonic
2022-06-07T23:25:39.041174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194612138
 
3.9%
19788368
 
2.7%
19797797
 
2.5%
19767700
 
2.4%
19777654
 
2.4%
19667379
 
2.3%
19756600
 
2.1%
19686335
 
2.0%
19676129
 
2.0%
19746064
 
1.9%
Other values (100)196896
62.6%
(Missing)41228
 
13.1%
ValueCountFrequency (%)
0208
0.1%
1951
 
< 0.1%
1961
 
< 0.1%
1971
 
< 0.1%
1992
 
< 0.1%
19103
 
< 0.1%
19111
 
< 0.1%
19122
 
< 0.1%
19131
 
< 0.1%
19161
 
< 0.1%
ValueCountFrequency (%)
20171250
 
0.4%
20163089
1.0%
20153240
1.0%
20143187
1.0%
20132984
0.9%
20122878
0.9%
20112717
0.9%
20102688
0.9%
20092914
0.9%
20084711
1.5%

TYPE REGISTRANT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing239
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.172944349
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:39.167678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.227868416
Coefficient of variation (CV)0.5650712666
Kurtosis0.2282121646
Mean2.172944349
Median Absolute Deviation (MAD)2
Skewness0.606700868
Sum682411
Variance1.507660847
MonotonicityNot monotonic
2022-06-07T23:25:39.444190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1150970
48.0%
3122149
38.9%
429637
 
9.4%
55783
 
1.8%
24427
 
1.4%
81070
 
0.3%
913
 
< 0.1%
(Missing)239
 
0.1%
ValueCountFrequency (%)
1150970
48.0%
24427
 
1.4%
3122149
38.9%
429637
 
9.4%
55783
 
1.8%
81070
 
0.3%
913
 
< 0.1%
ValueCountFrequency (%)
913
 
< 0.1%
81070
 
0.3%
55783
 
1.8%
429637
 
9.4%
3122149
38.9%
24427
 
1.4%
1150970
48.0%

NAME
Categorical

HIGH CARDINALITY

Distinct213600
Distinct (%)68.0%
Missing252
Missing (%)0.1%
Memory size2.4 MiB
SALE REPORTED
 
2853
WELLS FARGO BANK NORTHWEST NA TRUSTEE
 
2663
REGISTRATION PENDING
 
2352
BANK OF UTAH TRUSTEE
 
1048
AIRCRAFT GUARANTY CORP TRUSTEE
 
1004
Other values (213595)
304116 

Length

Max length50
Median length45
Mean length17.84704301
Min length2

Characters and Unicode

Total characters5604614
Distinct characters55
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177302 ?
Unique (%)56.5%

Sample

1st rowFEDERAL AVIATION ADMINISTRATION
2nd rowBENE MARY D
3rd rowPERRY AARON O
4th rowENGLISH MARK
5th rowCAMPBELL CHARLES N

Common Values

ValueCountFrequency (%)
SALE REPORTED2853
 
0.9%
WELLS FARGO BANK NORTHWEST NA TRUSTEE2663
 
0.8%
REGISTRATION PENDING2352
 
0.7%
BANK OF UTAH TRUSTEE1048
 
0.3%
AIRCRAFT GUARANTY CORP TRUSTEE1004
 
0.3%
DELTA AIR LINES INC950
 
0.3%
AMERICAN AIRLINES INC945
 
0.3%
SOUTHWEST AIRLINES CO662
 
0.2%
UNITED AIRLINES INC646
 
0.2%
SOUTHERN AIRCRAFT CONSULTANCY INC TRUSTEE603
 
0.2%
Other values (213590)300310
95.6%

Length

2022-06-07T23:25:39.620109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llc53726
 
5.4%
inc47606
 
4.8%
aviation20235
 
2.0%
a15785
 
1.6%
j14940
 
1.5%
l14766
 
1.5%
r12452
 
1.2%
trustee11879
 
1.2%
e11780
 
1.2%
air11748
 
1.2%
Other values (80831)783902
78.5%

Most occurring characters

ValueCountFrequency (%)
685075
12.2%
E480437
 
8.6%
A455846
 
8.1%
R421070
 
7.5%
I387583
 
6.9%
N379672
 
6.8%
L372728
 
6.7%
T290644
 
5.2%
O288338
 
5.1%
S281737
 
5.0%
Other values (45)1561484
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4890995
87.3%
Space Separator685075
 
12.2%
Decimal Number19891
 
0.4%
Other Punctuation5689
 
0.1%
Dash Punctuation2584
 
< 0.1%
Close Punctuation178
 
< 0.1%
Open Punctuation177
 
< 0.1%
Math Symbol25
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E480437
 
9.8%
A455846
 
9.3%
R421070
 
8.6%
I387583
 
7.9%
N379672
 
7.8%
L372728
 
7.6%
T290644
 
5.9%
O288338
 
5.9%
S281737
 
5.8%
C272853
 
5.6%
Other values (16)1260087
25.8%
Other Punctuation
ValueCountFrequency (%)
&4423
77.7%
'549
 
9.7%
/372
 
6.5%
.269
 
4.7%
#52
 
0.9%
!8
 
0.1%
?5
 
0.1%
@3
 
0.1%
:3
 
0.1%
"2
 
< 0.1%
Other values (3)3
 
0.1%
Decimal Number
ValueCountFrequency (%)
12985
15.0%
22878
14.5%
02270
11.4%
32057
10.3%
51946
9.8%
41854
9.3%
71604
8.1%
81493
7.5%
61479
7.4%
91325
6.7%
Math Symbol
ValueCountFrequency (%)
+24
96.0%
=1
 
4.0%
Space Separator
ValueCountFrequency (%)
685075
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2584
100.0%
Close Punctuation
ValueCountFrequency (%)
)178
100.0%
Open Punctuation
ValueCountFrequency (%)
(177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4890995
87.3%
Common713619
 
12.7%

Most frequent character per script

Common
ValueCountFrequency (%)
685075
96.0%
&4423
 
0.6%
12985
 
0.4%
22878
 
0.4%
-2584
 
0.4%
02270
 
0.3%
32057
 
0.3%
51946
 
0.3%
41854
 
0.3%
71604
 
0.2%
Other values (19)5943
 
0.8%
Latin
ValueCountFrequency (%)
E480437
 
9.8%
A455846
 
9.3%
R421070
 
8.6%
I387583
 
7.9%
N379672
 
7.8%
L372728
 
7.6%
T290644
 
5.9%
O288338
 
5.9%
S281737
 
5.8%
C272853
 
5.6%
Other values (16)1260087
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5604614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
685075
12.2%
E480437
 
8.6%
A455846
 
8.1%
R421070
 
7.5%
I387583
 
6.9%
N379672
 
6.8%
L372728
 
6.7%
T290644
 
5.2%
O288338
 
5.1%
S281737
 
5.0%
Other values (45)1561484
27.9%

STREET
Categorical

HIGH CARDINALITY

Distinct188558
Distinct (%)60.1%
Missing501
Missing (%)0.2%
Memory size2.4 MiB
3511 SILVERSIDE RD STE 105
 
3512
2711 CENTERVILLE RD STE 400
 
1114
MAC U1228-51
 
1048
200 E SOUTH TEMPLE STE 210
 
1017
1775 M H JACKSON SERVICE RD
 
709
Other values (188553)
306387 

Length

Max length33
Median length28
Mean length16.79418523
Min length3

Characters and Unicode

Total characters5269797
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152332 ?
Unique (%)48.5%

Sample

1st rowWASHINGTON REAGAN NATIONAL ARPT
2nd rowPO BOX 329
3rd rowPO BOX 736
4th row655 DOESKIN TRL
5th row604 CORDOVA CT

Common Values

ValueCountFrequency (%)
3511 SILVERSIDE RD STE 1053512
 
1.1%
2711 CENTERVILLE RD STE 4001114
 
0.4%
MAC U1228-511048
 
0.3%
200 E SOUTH TEMPLE STE 2101017
 
0.3%
1775 M H JACKSON SERVICE RD709
 
0.2%
16192 COASTAL HWY597
 
0.2%
PO BOX 2547515
 
0.2%
PO BOX 2549485
 
0.2%
2702 LOVE FIELD DR # HDQ-4GC460
 
0.1%
MAC: U1228-51424
 
0.1%
Other values (188548)303906
96.7%
(Missing)501
 
0.2%

Length

2022-06-07T23:25:39.814236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rd64556
 
5.6%
dr42722
 
3.7%
box40440
 
3.5%
po38061
 
3.3%
st37504
 
3.2%
ave26770
 
2.3%
ste20005
 
1.7%
n16126
 
1.4%
s15523
 
1.3%
e15042
 
1.3%
Other values (66518)839697
72.6%

Most occurring characters

ValueCountFrequency (%)
842947
 
16.0%
R306697
 
5.8%
E302424
 
5.7%
1249308
 
4.7%
O241907
 
4.6%
A241352
 
4.6%
T218356
 
4.1%
D201787
 
3.8%
S199580
 
3.8%
0192718
 
3.7%
Other values (42)2272721
43.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3093791
58.7%
Decimal Number1315849
25.0%
Space Separator842947
 
16.0%
Other Punctuation11387
 
0.2%
Dash Punctuation5770
 
0.1%
Close Punctuation26
 
< 0.1%
Open Punctuation26
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R306697
 
9.9%
E302424
 
9.8%
O241907
 
7.8%
A241352
 
7.8%
T218356
 
7.1%
D201787
 
6.5%
S199580
 
6.5%
N192369
 
6.2%
L171511
 
5.5%
I149955
 
4.8%
Other values (16)867853
28.1%
Other Punctuation
ValueCountFrequency (%)
#5404
47.5%
/3638
31.9%
:1555
 
13.7%
.418
 
3.7%
&334
 
2.9%
'18
 
0.2%
%14
 
0.1%
@2
 
< 0.1%
"2
 
< 0.1%
\1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1249308
18.9%
0192718
14.6%
2165663
12.6%
5132142
10.0%
3128601
9.8%
4111784
8.5%
690709
 
6.9%
788919
 
6.8%
878962
 
6.0%
977043
 
5.9%
Space Separator
ValueCountFrequency (%)
842947
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5770
100.0%
Close Punctuation
ValueCountFrequency (%)
)26
100.0%
Open Punctuation
ValueCountFrequency (%)
(26
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3093791
58.7%
Common2176006
41.3%

Most frequent character per script

Common
ValueCountFrequency (%)
842947
38.7%
1249308
 
11.5%
0192718
 
8.9%
2165663
 
7.6%
5132142
 
6.1%
3128601
 
5.9%
4111784
 
5.1%
690709
 
4.2%
788919
 
4.1%
878962
 
3.6%
Other values (16)94253
 
4.3%
Latin
ValueCountFrequency (%)
R306697
 
9.9%
E302424
 
9.8%
O241907
 
7.8%
A241352
 
7.8%
T218356
 
7.1%
D201787
 
6.5%
S199580
 
6.5%
N192369
 
6.2%
L171511
 
5.5%
I149955
 
4.8%
Other values (16)867853
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5269797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
842947
 
16.0%
R306697
 
5.8%
E302424
 
5.7%
1249308
 
4.7%
O241907
 
4.6%
A241352
 
4.6%
T218356
 
4.1%
D201787
 
3.8%
S199580
 
3.8%
0192718
 
3.7%
Other values (42)2272721
43.1%

STREET2
Categorical

HIGH CARDINALITY
MISSING

Distinct3818
Distinct (%)29.6%
Missing301400
Missing (%)95.9%
Memory size2.4 MiB
299 S MAIN ST FL 5
1743 
DEPT 595 AIRCRAFT REGISTRATIONS
 
529
1200 NW 63RD ST STE 5000
 
394
233 S WACKER DR
 
370
1100 N MARKET ST
 
320
Other values (3813)
9532 

Length

Max length33
Median length28
Mean length18.05353818
Min length1

Characters and Unicode

Total characters232674
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3121 ?
Unique (%)24.2%

Sample

1st row3201 THOMAS AVE HANGAR 6
2nd rowDITCHINGHAM BUNGAY
3rd rowDITCHINGHAM
4th row211 N BRIDGE ST
5th row# 158

Common Values

ValueCountFrequency (%)
299 S MAIN ST FL 51743
 
0.6%
DEPT 595 AIRCRAFT REGISTRATIONS529
 
0.2%
1200 NW 63RD ST STE 5000394
 
0.1%
233 S WACKER DR370
 
0.1%
1100 N MARKET ST320
 
0.1%
3131 DEMOCRAT RD292
 
0.1%
2207 CONCORD PIKE254
 
0.1%
MAC U1228-51245
 
0.1%
2955 REPUBLICAN DR232
 
0.1%
DEPT 595207
 
0.1%
Other values (3808)8302
 
2.6%
(Missing)301400
95.9%

Length

2022-06-07T23:25:40.104782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st3609
 
7.0%
s2639
 
5.1%
main2064
 
4.0%
fl1998
 
3.9%
2991922
 
3.7%
51801
 
3.5%
ste1547
 
3.0%
rd1466
 
2.9%
dr1210
 
2.4%
n834
 
1.6%
Other values (4427)32228
62.8%

Most occurring characters

ValueCountFrequency (%)
38461
16.5%
T13324
 
5.7%
A12842
 
5.5%
R12663
 
5.4%
S12042
 
5.2%
E11914
 
5.1%
N10039
 
4.3%
09632
 
4.1%
I8968
 
3.9%
18725
 
3.7%
Other values (36)94064
40.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter140382
60.3%
Decimal Number52366
 
22.5%
Space Separator38461
 
16.5%
Dash Punctuation894
 
0.4%
Other Punctuation551
 
0.2%
Open Punctuation10
 
< 0.1%
Close Punctuation10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T13324
 
9.5%
A12842
 
9.1%
R12663
 
9.0%
S12042
 
8.6%
E11914
 
8.5%
N10039
 
7.2%
I8968
 
6.4%
D7605
 
5.4%
L6928
 
4.9%
O6695
 
4.8%
Other values (16)37362
26.6%
Decimal Number
ValueCountFrequency (%)
09632
18.4%
18725
16.7%
27826
14.9%
57373
14.1%
96451
12.3%
34547
8.7%
62133
 
4.1%
71957
 
3.7%
41930
 
3.7%
81792
 
3.4%
Other Punctuation
ValueCountFrequency (%)
:237
43.0%
#174
31.6%
/71
 
12.9%
.60
 
10.9%
&6
 
1.1%
'3
 
0.5%
Space Separator
ValueCountFrequency (%)
38461
100.0%
Dash Punctuation
ValueCountFrequency (%)
-894
100.0%
Open Punctuation
ValueCountFrequency (%)
(10
100.0%
Close Punctuation
ValueCountFrequency (%)
)10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin140382
60.3%
Common92292
39.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T13324
 
9.5%
A12842
 
9.1%
R12663
 
9.0%
S12042
 
8.6%
E11914
 
8.5%
N10039
 
7.2%
I8968
 
6.4%
D7605
 
5.4%
L6928
 
4.9%
O6695
 
4.8%
Other values (16)37362
26.6%
Common
ValueCountFrequency (%)
38461
41.7%
09632
 
10.4%
18725
 
9.5%
27826
 
8.5%
57373
 
8.0%
96451
 
7.0%
34547
 
4.9%
62133
 
2.3%
71957
 
2.1%
41930
 
2.1%
Other values (10)3257
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII232674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38461
16.5%
T13324
 
5.7%
A12842
 
5.5%
R12663
 
5.4%
S12042
 
5.2%
E11914
 
5.1%
N10039
 
4.3%
09632
 
4.1%
I8968
 
3.9%
18725
 
3.7%
Other values (36)94064
40.4%

CITY
Categorical

HIGH CARDINALITY

Distinct15202
Distinct (%)4.8%
Missing239
Missing (%)0.1%
Memory size2.4 MiB
WILMINGTON
 
9247
SALT LAKE CITY
 
4160
ANCHORAGE
 
2717
DALLAS
 
1821
FORT WORTH
 
1691
Other values (15197)
294413 

Length

Max length18
Median length15
Mean length8.787803814
Min length2

Characters and Unicode

Total characters2759801
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3971 ?
Unique (%)1.3%

Sample

1st rowWASHINGTON
2nd rowKETCHUM
3rd rowMULBERRY
4th rowSANTA MARIA
5th rowSALISBURY

Common Values

ValueCountFrequency (%)
WILMINGTON9247
 
2.9%
SALT LAKE CITY4160
 
1.3%
ANCHORAGE2717
 
0.9%
DALLAS1821
 
0.6%
FORT WORTH1691
 
0.5%
ATLANTA1655
 
0.5%
HOUSTON1626
 
0.5%
LAS VEGAS1322
 
0.4%
PHOENIX1267
 
0.4%
OKLAHOMA CITY1239
 
0.4%
Other values (15192)287304
91.4%

Length

2022-06-07T23:25:40.379587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city12387
 
3.1%
wilmington9253
 
2.3%
lake7934
 
2.0%
salt4440
 
1.1%
beach4253
 
1.0%
fort4158
 
1.0%
san4044
 
1.0%
anchorage2717
 
0.7%
new2676
 
0.7%
springs2340
 
0.6%
Other values (12027)351706
86.6%

Most occurring characters

ValueCountFrequency (%)
A272913
 
9.9%
E255234
 
9.2%
L222197
 
8.1%
N220306
 
8.0%
O214592
 
7.8%
I180450
 
6.5%
R177632
 
6.4%
T160649
 
5.8%
S153823
 
5.6%
91871
 
3.3%
Other values (34)810134
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2667699
96.7%
Space Separator91871
 
3.3%
Decimal Number180
 
< 0.1%
Dash Punctuation27
 
< 0.1%
Other Punctuation20
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A272913
 
10.2%
E255234
 
9.6%
L222197
 
8.3%
N220306
 
8.3%
O214592
 
8.0%
I180450
 
6.8%
R177632
 
6.7%
T160649
 
6.0%
S153823
 
5.8%
C91394
 
3.4%
Other values (16)718509
26.9%
Decimal Number
ValueCountFrequency (%)
226
14.4%
026
14.4%
522
12.2%
122
12.2%
917
9.4%
315
8.3%
415
8.3%
613
7.2%
813
7.2%
711
6.1%
Other Punctuation
ValueCountFrequency (%)
'10
50.0%
/4
 
20.0%
.4
 
20.0%
&2
 
10.0%
Space Separator
ValueCountFrequency (%)
91871
100.0%
Dash Punctuation
ValueCountFrequency (%)
-27
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2667699
96.7%
Common92102
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A272913
 
10.2%
E255234
 
9.6%
L222197
 
8.3%
N220306
 
8.3%
O214592
 
8.0%
I180450
 
6.8%
R177632
 
6.7%
T160649
 
6.0%
S153823
 
5.8%
C91394
 
3.4%
Other values (16)718509
26.9%
Common
ValueCountFrequency (%)
91871
99.7%
-27
 
< 0.1%
226
 
< 0.1%
026
 
< 0.1%
522
 
< 0.1%
122
 
< 0.1%
917
 
< 0.1%
315
 
< 0.1%
415
 
< 0.1%
613
 
< 0.1%
Other values (8)48
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2759801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A272913
 
9.9%
E255234
 
9.2%
L222197
 
8.1%
N220306
 
8.0%
O214592
 
7.8%
I180450
 
6.5%
R177632
 
6.4%
T160649
 
5.8%
S153823
 
5.6%
91871
 
3.3%
Other values (34)810134
29.4%

STATE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)< 0.1%
Missing1573
Missing (%)0.5%
Memory size2.4 MiB
TX
29407 
CA
28865 
FL
21159 
DE
 
11893
WA
 
10552
Other values (54)
210839 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625430
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDC
2nd rowOK
3rd rowFL
4th rowCA
5th rowNC

Common Values

ValueCountFrequency (%)
TX29407
 
9.4%
CA28865
 
9.2%
FL21159
 
6.7%
DE11893
 
3.8%
WA10552
 
3.4%
AK9377
 
3.0%
IL8559
 
2.7%
GA8432
 
2.7%
OH8294
 
2.6%
AZ8249
 
2.6%
Other values (49)167928
53.4%

Length

2022-06-07T23:25:40.508458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx29407
 
9.4%
ca28865
 
9.2%
fl21159
 
6.8%
de11893
 
3.8%
wa10552
 
3.4%
ak9377
 
3.0%
il8559
 
2.7%
ga8432
 
2.7%
oh8294
 
2.7%
az8249
 
2.6%
Other values (49)167928
53.7%

Most occurring characters

ValueCountFrequency (%)
A97683
15.6%
N50723
 
8.1%
T49923
 
8.0%
C48596
 
7.8%
M38894
 
6.2%
L38730
 
6.2%
I36960
 
5.9%
O34825
 
5.6%
X29407
 
4.7%
K23795
 
3.8%
Other values (14)175894
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter625430
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A97683
15.6%
N50723
 
8.1%
T49923
 
8.0%
C48596
 
7.8%
M38894
 
6.2%
L38730
 
6.2%
I36960
 
5.9%
O34825
 
5.6%
X29407
 
4.7%
K23795
 
3.8%
Other values (14)175894
28.1%

Most occurring scripts

ValueCountFrequency (%)
Latin625430
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A97683
15.6%
N50723
 
8.1%
T49923
 
8.0%
C48596
 
7.8%
M38894
 
6.2%
L38730
 
6.2%
I36960
 
5.9%
O34825
 
5.6%
X29407
 
4.7%
K23795
 
3.8%
Other values (14)175894
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A97683
15.6%
N50723
 
8.1%
T49923
 
8.0%
C48596
 
7.8%
M38894
 
6.2%
L38730
 
6.2%
I36960
 
5.9%
O34825
 
5.6%
X29407
 
4.7%
K23795
 
3.8%
Other values (14)175894
28.1%

ZIP CODE
Categorical

HIGH CARDINALITY

Distinct180646
Distinct (%)57.6%
Missing502
Missing (%)0.2%
Memory size2.4 MiB
198104902
 
3765
841112689
 
1806
198081645
 
1138
841111346
 
989
84111
 
799
Other values (180641)
305289 

Length

Max length10
Median length9
Mean length8.483010714
Min length2

Characters and Unicode

Total characters2661850
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138412 ?
Unique (%)44.1%

Sample

1st row20001
2nd row743490329
3rd row338600736
4th row934556020
5th row281466337

Common Values

ValueCountFrequency (%)
1981049023765
 
1.2%
8411126891806
 
0.6%
1980816451138
 
0.4%
841111346989
 
0.3%
84111799
 
0.3%
303543743713
 
0.2%
199583608605
 
0.2%
606067147556
 
0.2%
752351908553
 
0.2%
773602547515
 
0.2%
Other values (180636)302347
96.2%

Length

2022-06-07T23:25:40.789993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1981049023765
 
1.2%
8411126891806
 
0.6%
1980816451138
 
0.4%
841111346989
 
0.3%
84111799
 
0.3%
303543743713
 
0.2%
199583608605
 
0.2%
606067147556
 
0.2%
752351908553
 
0.2%
773602547515
 
0.2%
Other values (180712)302918
96.4%

Most occurring characters

ValueCountFrequency (%)
0344060
12.9%
1314765
11.8%
3280529
10.5%
2274989
10.3%
5253419
9.5%
4251482
9.4%
9248715
9.3%
7240441
9.0%
8225788
8.5%
6224006
8.4%
Other values (28)3656
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2658194
99.9%
Uppercase Letter3015
 
0.1%
Space Separator572
 
< 0.1%
Dash Punctuation69
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N1258
41.7%
R621
20.6%
D619
20.5%
A57
 
1.9%
L40
 
1.3%
S39
 
1.3%
E37
 
1.2%
B34
 
1.1%
H33
 
1.1%
V33
 
1.1%
Other values (16)244
 
8.1%
Decimal Number
ValueCountFrequency (%)
0344060
12.9%
1314765
11.8%
3280529
10.6%
2274989
10.3%
5253419
9.5%
4251482
9.5%
9248715
9.4%
7240441
9.0%
8225788
8.5%
6224006
8.4%
Space Separator
ValueCountFrequency (%)
572
100.0%
Dash Punctuation
ValueCountFrequency (%)
-69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2658835
99.9%
Latin3015
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1258
41.7%
R621
20.6%
D619
20.5%
A57
 
1.9%
L40
 
1.3%
S39
 
1.3%
E37
 
1.2%
B34
 
1.1%
H33
 
1.1%
V33
 
1.1%
Other values (16)244
 
8.1%
Common
ValueCountFrequency (%)
0344060
12.9%
1314765
11.8%
3280529
10.6%
2274989
10.3%
5253419
9.5%
4251482
9.5%
9248715
9.4%
7240441
9.0%
8225788
8.5%
6224006
8.4%
Other values (2)641
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2661850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0344060
12.9%
1314765
11.8%
3280529
10.5%
2274989
10.3%
5253419
9.5%
4251482
9.4%
9248715
9.3%
7240441
9.0%
8225788
8.5%
6224006
8.4%
Other values (28)3656
 
0.1%

REGION
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing106
Missing (%)< 0.1%
Memory size2.4 MiB
2
49533 
C
48136 
1
46262 
4
42835 
S
42640 
Other values (5)
84776 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314182
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row7
4th row4
5th row1

Common Values

ValueCountFrequency (%)
249533
15.8%
C48136
15.3%
146262
14.7%
442835
13.6%
S42640
13.6%
738029
12.1%
326259
8.4%
E10113
 
3.2%
59377
 
3.0%
8998
 
0.3%
(Missing)106
 
< 0.1%

Length

2022-06-07T23:25:41.256649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-07T23:25:41.847822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
249533
15.8%
c48136
15.3%
146262
14.7%
442835
13.6%
s42640
13.6%
738029
12.1%
326259
8.4%
e10113
 
3.2%
59377
 
3.0%
8998
 
0.3%

Most occurring characters

ValueCountFrequency (%)
249533
15.8%
C48136
15.3%
146262
14.7%
442835
13.6%
S42640
13.6%
738029
12.1%
326259
8.4%
E10113
 
3.2%
59377
 
3.0%
8998
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number213293
67.9%
Uppercase Letter100889
32.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
249533
23.2%
146262
21.7%
442835
20.1%
738029
17.8%
326259
12.3%
59377
 
4.4%
8998
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C48136
47.7%
S42640
42.3%
E10113
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common213293
67.9%
Latin100889
32.1%

Most frequent character per script

Common
ValueCountFrequency (%)
249533
23.2%
146262
21.7%
442835
20.1%
738029
17.8%
326259
12.3%
59377
 
4.4%
8998
 
0.5%
Latin
ValueCountFrequency (%)
C48136
47.7%
S42640
42.3%
E10113
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII314182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
249533
15.8%
C48136
15.3%
146262
14.7%
442835
13.6%
S42640
13.6%
738029
12.1%
326259
8.4%
E10113
 
3.2%
59377
 
3.0%
8998
 
0.3%

COUNTY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct331
Distinct (%)0.1%
Missing1783
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean88.5622822
Minimum0
Maximum999
Zeros491
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:42.585588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q125
median61
Q3113
95-th percentile293
Maximum999
Range999
Interquartile range (IQR)88

Descriptive statistics

Standard deviation104.5335984
Coefficient of variation (CV)1.180339934
Kurtosis11.60198137
Mean88.5622822
Median Absolute Deviation (MAD)42
Skewness2.951654447
Sum27676156
Variance10927.27319
MonotonicityNot monotonic
2022-06-07T23:25:43.184174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316751
 
5.3%
138921
 
2.8%
358256
 
2.6%
18069
 
2.6%
377091
 
2.3%
316961
 
2.2%
56444
 
2.1%
295602
 
1.8%
255512
 
1.8%
195494
 
1.7%
Other values (321)233404
74.3%
ValueCountFrequency (%)
0491
 
0.2%
18069
2.6%
21
 
< 0.1%
316751
5.3%
45
 
< 0.1%
56444
 
2.1%
67
 
< 0.1%
73234
 
1.0%
94354
 
1.4%
1039
 
< 0.1%
ValueCountFrequency (%)
9993
 
< 0.1%
84070
 
< 0.1%
83088
 
< 0.1%
82019
 
< 0.1%
810345
0.1%
80052
 
< 0.1%
79016
 
< 0.1%
77533
 
< 0.1%
77080
 
< 0.1%
760187
0.1%

COUNTRY
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)< 0.1%
Missing240
Missing (%)0.1%
Memory size2.4 MiB
US
311863 
GB
 
658
RQ
 
572
VI
 
143
GU
 
99
Other values (80)
 
713

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters628096
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US311863
99.2%
GB658
 
0.2%
RQ572
 
0.2%
VI143
 
< 0.1%
GU99
 
< 0.1%
DE95
 
< 0.1%
CA91
 
< 0.1%
VU72
 
< 0.1%
MP36
 
< 0.1%
SA35
 
< 0.1%
Other values (75)384
 
0.1%
(Missing)240
 
0.1%

Length

2022-06-07T23:25:43.490940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us311863
99.3%
gb658
 
0.2%
rq572
 
0.2%
vi143
 
< 0.1%
gu99
 
< 0.1%
de95
 
< 0.1%
ca91
 
< 0.1%
vu72
 
< 0.1%
mp36
 
< 0.1%
sa35
 
< 0.1%
Other values (75)384
 
0.1%

Most occurring characters

ValueCountFrequency (%)
U312052
49.7%
S311943
49.7%
G769
 
0.1%
B712
 
0.1%
R622
 
0.1%
Q574
 
0.1%
V223
 
< 0.1%
A200
 
< 0.1%
I169
 
< 0.1%
E144
 
< 0.1%
Other values (16)688
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter628096
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U312052
49.7%
S311943
49.7%
G769
 
0.1%
B712
 
0.1%
R622
 
0.1%
Q574
 
0.1%
V223
 
< 0.1%
A200
 
< 0.1%
I169
 
< 0.1%
E144
 
< 0.1%
Other values (16)688
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin628096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U312052
49.7%
S311943
49.7%
G769
 
0.1%
B712
 
0.1%
R622
 
0.1%
Q574
 
0.1%
V223
 
< 0.1%
A200
 
< 0.1%
I169
 
< 0.1%
E144
 
< 0.1%
Other values (16)688
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII628096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U312052
49.7%
S311943
49.7%
G769
 
0.1%
B712
 
0.1%
R622
 
0.1%
Q574
 
0.1%
V223
 
< 0.1%
A200
 
< 0.1%
I169
 
< 0.1%
E144
 
< 0.1%
Other values (16)688
 
0.1%

LAST ACTION DATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3480
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20147139.23
Minimum19720113
Maximum20170724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:43.846786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19720113
5-th percentile20120427
Q120150409
median20160309
Q320161213
95-th percentile20170607
Maximum20170724
Range450611
Interquartile range (IQR)10804

Descriptive statistics

Standard deviation57690.87421
Coefficient of variation (CV)0.002863477219
Kurtosis35.62143853
Mean20147139.23
Median Absolute Deviation (MAD)9797
Skewness-5.942645526
Sum6.332004094 × 1012
Variance3328236967
MonotonicityNot monotonic
2022-06-07T23:25:44.046029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197701145959
 
1.9%
201705091454
 
0.5%
201705081125
 
0.4%
201705101018
 
0.3%
20170213983
 
0.3%
20160510960
 
0.3%
20170207900
 
0.3%
20161108899
 
0.3%
20161212888
 
0.3%
20160809887
 
0.3%
Other values (3470)299215
95.2%
ValueCountFrequency (%)
197201131
 
< 0.1%
197205251
 
< 0.1%
197304021
 
< 0.1%
197701145959
1.9%
197701156
 
< 0.1%
197701228
 
< 0.1%
197701294
 
< 0.1%
1977021312
 
< 0.1%
1977021923
 
< 0.1%
1977022215
 
< 0.1%
ValueCountFrequency (%)
20170724435
0.1%
2017072356
 
< 0.1%
2017072263
 
< 0.1%
20170721408
0.1%
20170720605
0.2%
20170719606
0.2%
20170718701
0.2%
20170717627
0.2%
2017071690
 
< 0.1%
2017071590
 
< 0.1%

CERT ISSUE DATE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12938
Distinct (%)4.3%
Missing12427
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean20079435.64
Minimum19401226
Maximum20170724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:44.216670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19401226
5-th percentile19870821
Q120040913
median20111206
Q320150514
95-th percentile20170228
Maximum20170724
Range769498
Interquartile range (IQR)109601

Descriptive statistics

Standard deviation97071.93732
Coefficient of variation (CV)0.0048343957
Kurtosis3.082391067
Mean20079435.64
Median Absolute Deviation (MAD)41001
Skewness-1.710558093
Sum6.061198522 × 1012
Variance9422961015
MonotonicityNot monotonic
2022-06-07T23:25:44.391167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20151205303
 
0.1%
20130401288
 
0.1%
20091231285
 
0.1%
20151230265
 
0.1%
20151212255
 
0.1%
20161212254
 
0.1%
20170104254
 
0.1%
20151219250
 
0.1%
20170201246
 
0.1%
20170509245
 
0.1%
Other values (12928)299216
95.2%
(Missing)12427
 
4.0%
ValueCountFrequency (%)
194012261
< 0.1%
194312161
< 0.1%
194604041
< 0.1%
194605291
< 0.1%
194607111
< 0.1%
194607261
< 0.1%
194609051
< 0.1%
194610241
< 0.1%
194705211
< 0.1%
194709091
< 0.1%
ValueCountFrequency (%)
20170724193
0.1%
20170721160
0.1%
20170720239
0.1%
20170719239
0.1%
20170718213
0.1%
20170717155
< 0.1%
20170714138
< 0.1%
20170713204
0.1%
2017071269
 
< 0.1%
2017071183
 
< 0.1%

CERTIFICATION
Categorical

HIGH CARDINALITY
MISSING

Distinct305
Distinct (%)0.1%
Missing27905
Missing (%)8.9%
Memory size2.4 MiB
1N
96422 
1
50419 
1NU
32434 
42
28400 
1T
19982 
Other values (300)
58726 

Length

Max length10
Median length2
Mean length2.008174368
Min length1

Characters and Unicode

Total characters575107
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)< 0.1%

Sample

1st row1T
2nd row1
3rd row1
4th row31
5th row1U

Common Values

ValueCountFrequency (%)
1N96422
30.7%
150419
16.0%
1NU32434
 
10.3%
4228400
 
9.0%
1T19982
 
6.4%
1U18226
 
5.8%
48A5590
 
1.8%
314565
 
1.5%
1B4522
 
1.4%
433764
 
1.2%
Other values (295)22059
 
7.0%
(Missing)27905
 
8.9%

Length

2022-06-07T23:25:44.556083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1n96422
33.7%
150419
17.6%
1nu32434
 
11.3%
4228400
 
9.9%
1t19982
 
7.0%
1u18226
 
6.4%
48a5590
 
2.0%
314565
 
1.6%
1b4522
 
1.6%
433764
 
1.3%
Other values (295)22059
 
7.7%

Most occurring characters

ValueCountFrequency (%)
1241380
42.0%
N131942
22.9%
U50980
 
8.9%
446075
 
8.0%
229610
 
5.1%
T19993
 
3.5%
316420
 
2.9%
A12596
 
2.2%
87065
 
1.2%
B5197
 
0.9%
Other values (11)13849
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number350364
60.9%
Uppercase Letter224743
39.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N131942
58.7%
U50980
 
22.7%
T19993
 
8.9%
A12596
 
5.6%
B5197
 
2.3%
C1934
 
0.9%
G1708
 
0.8%
W189
 
0.1%
P107
 
< 0.1%
O95
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1241380
68.9%
446075
 
13.2%
229610
 
8.5%
316420
 
4.7%
87065
 
2.0%
63445
 
1.0%
93128
 
0.9%
02617
 
0.7%
5473
 
0.1%
7151
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common350364
60.9%
Latin224743
39.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N131942
58.7%
U50980
 
22.7%
T19993
 
8.9%
A12596
 
5.6%
B5197
 
2.3%
C1934
 
0.9%
G1708
 
0.8%
W189
 
0.1%
P107
 
< 0.1%
O95
 
< 0.1%
Common
ValueCountFrequency (%)
1241380
68.9%
446075
 
13.2%
229610
 
8.5%
316420
 
4.7%
87065
 
2.0%
63445
 
1.0%
93128
 
0.9%
02617
 
0.7%
5473
 
0.1%
7151
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII575107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1241380
42.0%
N131942
22.9%
U50980
 
8.9%
446075
 
8.0%
229610
 
5.1%
T19993
 
3.5%
316420
 
2.9%
A12596
 
2.2%
87065
 
1.2%
B5197
 
0.9%
Other values (11)13849
 
2.4%

TYPE AIRCRAFT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.255717686
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:44.669077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8864904
Coefficient of variation (CV)0.2083057349
Kurtosis5.895051093
Mean4.255717686
Median Absolute Deviation (MAD)0
Skewness0.3716574108
Sum1337521
Variance0.7858652293
MonotonicityNot monotonic
2022-06-07T23:25:44.885852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4228478
72.7%
549385
 
15.7%
620973
 
6.7%
25670
 
1.8%
15355
 
1.7%
82664
 
0.8%
71400
 
0.4%
9325
 
0.1%
338
 
< 0.1%
ValueCountFrequency (%)
15355
 
1.7%
25670
 
1.8%
338
 
< 0.1%
4228478
72.7%
549385
 
15.7%
620973
 
6.7%
71400
 
0.4%
82664
 
0.8%
9325
 
0.1%
ValueCountFrequency (%)
9325
 
0.1%
82664
 
0.8%
71400
 
0.4%
620973
 
6.7%
549385
 
15.7%
4228478
72.7%
338
 
< 0.1%
25670
 
1.8%
15355
 
1.7%

TYPE ENGINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.113987807
Minimum0
Maximum11
Zeros10111
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:45.005103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.366856881
Coefficient of variation (CV)1.119617092
Kurtosis2.502229558
Mean2.113987807
Median Absolute Deviation (MAD)0
Skewness1.93397783
Sum664401
Variance5.602011495
MonotonicityNot monotonic
2022-06-07T23:25:45.129514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1225879
71.9%
522201
 
7.1%
819316
 
6.1%
212336
 
3.9%
010111
 
3.2%
38796
 
2.8%
76867
 
2.2%
106386
 
2.0%
42331
 
0.7%
1153
 
< 0.1%
Other values (2)12
 
< 0.1%
ValueCountFrequency (%)
010111
 
3.2%
1225879
71.9%
212336
 
3.9%
38796
 
2.8%
42331
 
0.7%
522201
 
7.1%
65
 
< 0.1%
76867
 
2.2%
819316
 
6.1%
97
 
< 0.1%
ValueCountFrequency (%)
1153
 
< 0.1%
106386
 
2.0%
97
 
< 0.1%
819316
6.1%
76867
 
2.2%
65
 
< 0.1%
522201
7.1%
42331
 
0.7%
38796
 
2.8%
212336
3.9%

STATUS CODE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
V
271577 
28
 
12884
25
 
10325
9
 
4855
24
 
3965
Other values (21)
 
10682

Length

Max length2
Median length1
Mean length1.101702897
Min length1

Characters and Unicode

Total characters346252
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowV
2nd rowV
3rd rowV
4th rowV
5th rowV

Common Values

ValueCountFrequency (%)
V271577
86.4%
2812884
 
4.1%
2510325
 
3.3%
94855
 
1.5%
243965
 
1.3%
72685
 
0.9%
R2307
 
0.7%
262197
 
0.7%
211573
 
0.5%
27671
 
0.2%
Other values (16)1249
 
0.4%

Length

2022-06-07T23:25:45.270620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v271577
86.4%
2812884
 
4.1%
2510325
 
3.3%
94855
 
1.5%
243965
 
1.3%
72685
 
0.9%
r2307
 
0.7%
262197
 
0.7%
211573
 
0.5%
27671
 
0.2%
Other values (16)1249
 
0.4%

Most occurring characters

ValueCountFrequency (%)
V271577
78.4%
231748
 
9.2%
812884
 
3.7%
510325
 
3.0%
94904
 
1.4%
44010
 
1.2%
73527
 
1.0%
R2307
 
0.7%
62209
 
0.6%
11902
 
0.5%
Other values (6)859
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter274618
79.3%
Decimal Number71634
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
231748
44.3%
812884
18.0%
510325
 
14.4%
94904
 
6.8%
44010
 
5.6%
73527
 
4.9%
62209
 
3.1%
11902
 
2.7%
363
 
0.1%
062
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
V271577
98.9%
R2307
 
0.8%
M650
 
0.2%
D49
 
< 0.1%
N31
 
< 0.1%
W4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin274618
79.3%
Common71634
 
20.7%

Most frequent character per script

Common
ValueCountFrequency (%)
231748
44.3%
812884
18.0%
510325
 
14.4%
94904
 
6.8%
44010
 
5.6%
73527
 
4.9%
62209
 
3.1%
11902
 
2.7%
363
 
0.1%
062
 
0.1%
Latin
ValueCountFrequency (%)
V271577
98.9%
R2307
 
0.8%
M650
 
0.2%
D49
 
< 0.1%
N31
 
< 0.1%
W4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII346252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V271577
78.4%
231748
 
9.2%
812884
 
3.7%
510325
 
3.0%
94904
 
1.4%
44010
 
1.2%
73527
 
1.0%
R2307
 
0.7%
62209
 
0.6%
11902
 
0.5%
Other values (6)859
 
0.2%

MODE S CODE
Real number (ℝ≥0)

UNIQUE

Distinct314288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51558527.51
Minimum50000001
Maximum53373706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:45.425950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50000001
5-th percentile50100600.35
Q150607768.75
median51503034.5
Q352422504
95-th percentile53221175.65
Maximum53373706
Range3373705
Interquartile range (IQR)1814735.25

Descriptive statistics

Standard deviation1014127.09
Coefficient of variation (CV)0.01966943469
Kurtosis-1.231781113
Mean51558527.51
Median Absolute Deviation (MAD)911612
Skewness0.1184621479
Sum1.620422649 × 1013
Variance1.028453754 × 1012
MonotonicityNot monotonic
2022-06-07T23:25:45.603023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000011
 
< 0.1%
522144661
 
< 0.1%
522144451
 
< 0.1%
522144431
 
< 0.1%
522144401
 
< 0.1%
522144371
 
< 0.1%
522144361
 
< 0.1%
522144331
 
< 0.1%
522144711
 
< 0.1%
522144651
 
< 0.1%
Other values (314278)314278
> 99.9%
ValueCountFrequency (%)
500000011
< 0.1%
500000021
< 0.1%
500000051
< 0.1%
500000061
< 0.1%
500000111
< 0.1%
500000121
< 0.1%
500000131
< 0.1%
500000151
< 0.1%
500000161
< 0.1%
500000171
< 0.1%
ValueCountFrequency (%)
533737061
< 0.1%
533737051
< 0.1%
533737001
< 0.1%
533736771
< 0.1%
533736761
< 0.1%
533736731
< 0.1%
533736721
< 0.1%
533736701
< 0.1%
533736671
< 0.1%
533736661
< 0.1%

FRACT OWNER
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing313488
Missing (%)99.7%
Memory size614.0 KiB
True
 
800
(Missing)
313488 
ValueCountFrequency (%)
True800
 
0.3%
(Missing)313488
99.7%
2022-06-07T23:25:45.764351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

AIR WORTH DATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21258
Distinct (%)7.8%
Missing40839
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean19841311.91
Minimum19000217
Maximum20170710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:45.890013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19000217
5-th percentile19560425
Q119671228
median19800131
Q320030509
95-th percentile20140630
Maximum20170710
Range1170493
Interquartile range (IQR)359281

Descriptive statistics

Standard deviation193016.2932
Coefficient of variation (CV)0.009728000548
Kurtosis-1.314682358
Mean19841311.91
Median Absolute Deviation (MAD)170779
Skewness0.109890939
Sum5.425586901 × 1012
Variance3.725528942 × 1010
MonotonicityNot monotonic
2022-06-07T23:25:46.066790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19560716405
 
0.1%
19560714254
 
0.1%
19790815217
 
0.1%
19791112171
 
0.1%
19591027163
 
0.1%
19611024158
 
0.1%
19601031155
 
< 0.1%
19560715152
 
< 0.1%
19560712139
 
< 0.1%
19560505127
 
< 0.1%
Other values (21248)271508
86.4%
(Missing)40839
 
13.0%
ValueCountFrequency (%)
190002171
< 0.1%
190512181
< 0.1%
190601081
< 0.1%
190609281
< 0.1%
191007181
< 0.1%
191205081
< 0.1%
191611041
< 0.1%
191612101
< 0.1%
191706271
< 0.1%
191709221
< 0.1%
ValueCountFrequency (%)
201707101
 
< 0.1%
201707072
 
< 0.1%
201707051
 
< 0.1%
201707023
 
< 0.1%
201707011
 
< 0.1%
201706304
< 0.1%
201706295
< 0.1%
201706284
< 0.1%
201706274
< 0.1%
201706269
< 0.1%

OTHER NAMES(1)
Categorical

HIGH CARDINALITY
MISSING

Distinct32698
Distinct (%)85.9%
Missing276202
Missing (%)87.9%
Memory size2.4 MiB
US CUSTOMS & BORDER PROTECTION
 
79
NICHOLS JUDITH A TRUSTEE
 
63
U S CUSTOMS & BORDER PROTECTION
 
50
CAMERON BALLOONS US
 
45
DEPARTMENT OF PUBLIC SAFETY
 
43
Other values (32693)
37806 

Length

Max length50
Median length46
Mean length16.60035184
Min length1

Characters and Unicode

Total characters632241
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29838 ?
Unique (%)78.3%

Sample

1st rowENGLISH TRACY
2nd rowAERO FLIGHT AVIATION
3rd rowPOLICE TRAINING DIVISION
4th rowVALDEZ REBECCA A
5th rowANDERSON ISAAC J

Common Values

ValueCountFrequency (%)
US CUSTOMS & BORDER PROTECTION79
 
< 0.1%
NICHOLS JUDITH A TRUSTEE63
 
< 0.1%
U S CUSTOMS & BORDER PROTECTION50
 
< 0.1%
CAMERON BALLOONS US45
 
< 0.1%
DEPARTMENT OF PUBLIC SAFETY43
 
< 0.1%
AIRWARE36
 
< 0.1%
J W DUFF AIRCRAFT CO33
 
< 0.1%
OFFICE OF AVIATION SERVICES31
 
< 0.1%
AVIATION CENTER LOGISTICS COMMAND28
 
< 0.1%
BOARD OF TRUSTEES26
 
< 0.1%
Other values (32688)37652
 
12.0%
(Missing)276202
87.9%

Length

2022-06-07T23:25:46.256635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l3401
 
3.0%
a3325
 
2.9%
m2838
 
2.5%
j2501
 
2.2%
trustee1928
 
1.7%
e1663
 
1.5%
r1464
 
1.3%
d1440
 
1.3%
s1306
 
1.1%
c1256
 
1.1%
Other values (20672)92669
81.4%

Most occurring characters

ValueCountFrequency (%)
75770
12.0%
E61572
 
9.7%
A57617
 
9.1%
R48655
 
7.7%
N43307
 
6.8%
I37225
 
5.9%
L36511
 
5.8%
S32638
 
5.2%
T31683
 
5.0%
O31549
 
5.0%
Other values (38)175714
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter554830
87.8%
Space Separator75770
 
12.0%
Other Punctuation780
 
0.1%
Dash Punctuation425
 
0.1%
Decimal Number383
 
0.1%
Close Punctuation26
 
< 0.1%
Open Punctuation26
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E61572
11.1%
A57617
 
10.4%
R48655
 
8.8%
N43307
 
7.8%
I37225
 
6.7%
L36511
 
6.6%
S32638
 
5.9%
T31683
 
5.7%
O31549
 
5.7%
C21857
 
3.9%
Other values (16)152216
27.4%
Decimal Number
ValueCountFrequency (%)
062
16.2%
154
14.1%
249
12.8%
539
10.2%
338
9.9%
936
9.4%
633
8.6%
429
7.6%
724
 
6.3%
819
 
5.0%
Other Punctuation
ValueCountFrequency (%)
&500
64.1%
/179
 
22.9%
'56
 
7.2%
:23
 
2.9%
.20
 
2.6%
!1
 
0.1%
@1
 
0.1%
Space Separator
ValueCountFrequency (%)
75770
100.0%
Dash Punctuation
ValueCountFrequency (%)
-425
100.0%
Close Punctuation
ValueCountFrequency (%)
)26
100.0%
Open Punctuation
ValueCountFrequency (%)
(26
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin554830
87.8%
Common77411
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E61572
11.1%
A57617
 
10.4%
R48655
 
8.8%
N43307
 
7.8%
I37225
 
6.7%
L36511
 
6.6%
S32638
 
5.9%
T31683
 
5.7%
O31549
 
5.7%
C21857
 
3.9%
Other values (16)152216
27.4%
Common
ValueCountFrequency (%)
75770
97.9%
&500
 
0.6%
-425
 
0.5%
/179
 
0.2%
062
 
0.1%
'56
 
0.1%
154
 
0.1%
249
 
0.1%
539
 
0.1%
338
 
< 0.1%
Other values (12)239
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII632241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75770
12.0%
E61572
 
9.7%
A57617
 
9.1%
R48655
 
7.7%
N43307
 
6.8%
I37225
 
5.9%
L36511
 
5.8%
S32638
 
5.2%
T31683
 
5.0%
O31549
 
5.0%
Other values (38)175714
27.8%

OTHER NAMES(2)
Categorical

HIGH CARDINALITY
MISSING

Distinct3180
Distinct (%)88.3%
Missing310686
Missing (%)98.9%
Memory size2.4 MiB
OFFICE OF AIR & MARINE
 
113
AVIATION MANAGEMENT
 
19
WILMINGTON TRUST CO TRUSTEE
 
18
AIRCRAFT SECTION
 
14
JOLES CHRISTOPHER A
 
10
Other values (3175)
3428 

Length

Max length48
Median length39
Mean length16.78761799
Min length1

Characters and Unicode

Total characters60469
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3037 ?
Unique (%)84.3%

Sample

1st rowCOMMISSION OF FORESTRY
2nd rowSTENE ROBERT J
3rd rowGUILLORY HOWARD C
4th rowFINLEY ALFRED L
5th rowREDTAIL AVIATION LLC

Common Values

ValueCountFrequency (%)
OFFICE OF AIR & MARINE113
 
< 0.1%
AVIATION MANAGEMENT19
 
< 0.1%
WILMINGTON TRUST CO TRUSTEE18
 
< 0.1%
AIRCRAFT SECTION14
 
< 0.1%
JOLES CHRISTOPHER A10
 
< 0.1%
DIVISION OF FOREST RESOURCES10
 
< 0.1%
DEPARTMENT OF AVIATION10
 
< 0.1%
DEPARTMENT OF NATURAL RESOURCES10
 
< 0.1%
FORESTRY DIVISION8
 
< 0.1%
WELLS FARGO BANK NORTHWEST NA TRUSTEE8
 
< 0.1%
Other values (3170)3382
 
1.1%
(Missing)310686
98.9%

Length

2022-06-07T23:25:46.429147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llc367
 
3.4%
of235
 
2.2%
a216
 
2.0%
l180
 
1.6%
j180
 
1.6%
air163
 
1.5%
d160
 
1.5%
m151
 
1.4%
143
 
1.3%
inc136
 
1.2%
Other values (3568)8979
82.3%

Most occurring characters

ValueCountFrequency (%)
7311
12.1%
E5396
 
8.9%
A4920
 
8.1%
R4538
 
7.5%
I3986
 
6.6%
N3924
 
6.5%
L3587
 
5.9%
O3410
 
5.6%
S3039
 
5.0%
T2991
 
4.9%
Other values (36)17367
28.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter52826
87.4%
Space Separator7311
 
12.1%
Other Punctuation175
 
0.3%
Decimal Number120
 
0.2%
Dash Punctuation35
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E5396
 
10.2%
A4920
 
9.3%
R4538
 
8.6%
I3986
 
7.5%
N3924
 
7.4%
L3587
 
6.8%
O3410
 
6.5%
S3039
 
5.8%
T2991
 
5.7%
C2485
 
4.7%
Other values (16)14550
27.5%
Decimal Number
ValueCountFrequency (%)
023
19.2%
418
15.0%
217
14.2%
114
11.7%
512
10.0%
311
9.2%
77
 
5.8%
67
 
5.8%
97
 
5.8%
84
 
3.3%
Other Punctuation
ValueCountFrequency (%)
&146
83.4%
:10
 
5.7%
/8
 
4.6%
.7
 
4.0%
'3
 
1.7%
\1
 
0.6%
Space Separator
ValueCountFrequency (%)
7311
100.0%
Dash Punctuation
ValueCountFrequency (%)
-35
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin52826
87.4%
Common7643
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E5396
 
10.2%
A4920
 
9.3%
R4538
 
8.6%
I3986
 
7.5%
N3924
 
7.4%
L3587
 
6.8%
O3410
 
6.5%
S3039
 
5.8%
T2991
 
5.7%
C2485
 
4.7%
Other values (16)14550
27.5%
Common
ValueCountFrequency (%)
7311
95.7%
&146
 
1.9%
-35
 
0.5%
023
 
0.3%
418
 
0.2%
217
 
0.2%
114
 
0.2%
512
 
0.2%
311
 
0.1%
:10
 
0.1%
Other values (10)46
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII60469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7311
12.1%
E5396
 
8.9%
A4920
 
8.1%
R4538
 
7.5%
I3986
 
6.6%
N3924
 
6.5%
L3587
 
5.9%
O3410
 
5.6%
S3039
 
5.0%
T2991
 
4.9%
Other values (36)17367
28.7%

OTHER NAMES(3)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1067
Distinct (%)95.7%
Missing313173
Missing (%)99.6%
Memory size2.4 MiB
HEIDINGER JEREMY L
 
11
WELLS FARGO BANK NORTHWEST NA TRUSTEE
 
5
NEW LINE CINEMA LLC
 
4
WARREN INC
 
3
SCHULTZ TODD H
 
3
Other values (1062)
1089 

Length

Max length45
Median length35
Mean length16.73004484
Min length7

Characters and Unicode

Total characters18654
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)92.9%

Sample

1st rowTAYLOR ARCHIE R
2nd rowCARMAN M LESLIE TRUSTEE
3rd rowCHESAPEAKE ENERGY CORP
4th rowSHORE STEPHEN A
5th rowBOEN MICHAEL B

Common Values

ValueCountFrequency (%)
HEIDINGER JEREMY L11
 
< 0.1%
WELLS FARGO BANK NORTHWEST NA TRUSTEE5
 
< 0.1%
NEW LINE CINEMA LLC4
 
< 0.1%
WARREN INC3
 
< 0.1%
SCHULTZ TODD H3
 
< 0.1%
WILMINGTON TRUST CO TRUSTEE3
 
< 0.1%
SUMERS GARY2
 
< 0.1%
EDWARD R SCOTT JR PC2
 
< 0.1%
HOUSE OF BEN AVRAHAM LLC2
 
< 0.1%
AEROSLEEP LLC2
 
< 0.1%
Other values (1057)1078
 
0.3%
(Missing)313173
99.6%

Length

2022-06-07T23:25:46.581230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llc277
 
8.3%
inc80
 
2.4%
j55
 
1.7%
l52
 
1.6%
a52
 
1.6%
m38
 
1.1%
trustee37
 
1.1%
d34
 
1.0%
aviation33
 
1.0%
r30
 
0.9%
Other values (1504)2639
79.3%

Most occurring characters

ValueCountFrequency (%)
2212
11.9%
E1588
 
8.5%
A1542
 
8.3%
L1426
 
7.6%
R1361
 
7.3%
N1241
 
6.7%
I1068
 
5.7%
C960
 
5.1%
S959
 
5.1%
T910
 
4.9%
Other values (32)5387
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter16367
87.7%
Space Separator2212
 
11.9%
Decimal Number47
 
0.3%
Dash Punctuation14
 
0.1%
Other Punctuation12
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E1588
 
9.7%
A1542
 
9.4%
L1426
 
8.7%
R1361
 
8.3%
N1241
 
7.6%
I1068
 
6.5%
C960
 
5.9%
S959
 
5.9%
T910
 
5.6%
O895
 
5.5%
Other values (16)4417
27.0%
Decimal Number
ValueCountFrequency (%)
210
21.3%
09
19.1%
19
19.1%
38
17.0%
56
12.8%
41
 
2.1%
81
 
2.1%
71
 
2.1%
61
 
2.1%
91
 
2.1%
Other Punctuation
ValueCountFrequency (%)
&11
91.7%
/1
 
8.3%
Space Separator
ValueCountFrequency (%)
2212
100.0%
Dash Punctuation
ValueCountFrequency (%)
-14
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16367
87.7%
Common2287
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E1588
 
9.7%
A1542
 
9.4%
L1426
 
8.7%
R1361
 
8.3%
N1241
 
7.6%
I1068
 
6.5%
C960
 
5.9%
S959
 
5.9%
T910
 
5.6%
O895
 
5.5%
Other values (16)4417
27.0%
Common
ValueCountFrequency (%)
2212
96.7%
-14
 
0.6%
&11
 
0.5%
210
 
0.4%
09
 
0.4%
19
 
0.4%
38
 
0.3%
56
 
0.3%
41
 
< 0.1%
81
 
< 0.1%
Other values (6)6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2212
11.9%
E1588
 
8.5%
A1542
 
8.3%
L1426
 
7.6%
R1361
 
7.3%
N1241
 
6.7%
I1068
 
5.7%
C960
 
5.1%
S959
 
5.1%
T910
 
4.9%
Other values (32)5387
28.9%

OTHER NAMES(4)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct568
Distinct (%)95.0%
Missing313690
Missing (%)99.8%
Memory size2.4 MiB
WELLS FARGO BANK NORTHWEST NA TRUSTEE
 
9
WILMINGTON TRUST CO TRUSTEE
 
5
BANK OF UTAH TRUSTEE
 
4
MICROSOFT FINANCING CORP
 
4
MAYER ELECTRIC SUPPLY CO INC
 
3
Other values (563)
573 

Length

Max length45
Median length30
Mean length18.05183946
Min length7

Characters and Unicode

Total characters10795
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique555 ?
Unique (%)92.8%

Sample

1st rowBRUHL DAN B
2nd rowWELLS FARGO BANK NORTHWEST NA TRUSTEE
3rd rowSALESFORCE COM INC
4th rowR&E AVIATION INC
5th rowRSD AIRCRAFT CORP

Common Values

ValueCountFrequency (%)
WELLS FARGO BANK NORTHWEST NA TRUSTEE9
 
< 0.1%
WILMINGTON TRUST CO TRUSTEE5
 
< 0.1%
BANK OF UTAH TRUSTEE4
 
< 0.1%
MICROSOFT FINANCING CORP4
 
< 0.1%
MAYER ELECTRIC SUPPLY CO INC3
 
< 0.1%
VLOCK MICHAEL K3
 
< 0.1%
TURNER BROADCASTING SYSTEM INC3
 
< 0.1%
URBINA JEFFREY A2
 
< 0.1%
PADDOCK CHEVROLET2
 
< 0.1%
HAMMOND AVIATION LLC2
 
< 0.1%
Other values (558)561
 
0.2%
(Missing)313690
99.8%

Length

2022-06-07T23:25:46.731273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llc248
 
13.2%
inc61
 
3.2%
aviation43
 
2.3%
trustee42
 
2.2%
co30
 
1.6%
corp25
 
1.3%
air19
 
1.0%
management17
 
0.9%
l15
 
0.8%
bank14
 
0.7%
Other values (885)1365
72.6%

Most occurring characters

ValueCountFrequency (%)
1281
11.9%
L966
 
8.9%
A860
 
8.0%
E846
 
7.8%
R740
 
6.9%
N694
 
6.4%
I669
 
6.2%
T643
 
6.0%
C624
 
5.8%
O545
 
5.0%
Other values (27)2927
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9470
87.7%
Space Separator1281
 
11.9%
Decimal Number17
 
0.2%
Other Punctuation15
 
0.1%
Dash Punctuation12
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L966
10.2%
A860
 
9.1%
E846
 
8.9%
R740
 
7.8%
N694
 
7.3%
I669
 
7.1%
T643
 
6.8%
C624
 
6.6%
O545
 
5.8%
S524
 
5.5%
Other values (16)2359
24.9%
Decimal Number
ValueCountFrequency (%)
24
23.5%
34
23.5%
12
11.8%
42
11.8%
72
11.8%
01
 
5.9%
51
 
5.9%
61
 
5.9%
Space Separator
ValueCountFrequency (%)
1281
100.0%
Other Punctuation
ValueCountFrequency (%)
&15
100.0%
Dash Punctuation
ValueCountFrequency (%)
-12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9470
87.7%
Common1325
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L966
10.2%
A860
 
9.1%
E846
 
8.9%
R740
 
7.8%
N694
 
7.3%
I669
 
7.1%
T643
 
6.8%
C624
 
6.6%
O545
 
5.8%
S524
 
5.5%
Other values (16)2359
24.9%
Common
ValueCountFrequency (%)
1281
96.7%
&15
 
1.1%
-12
 
0.9%
24
 
0.3%
34
 
0.3%
12
 
0.2%
42
 
0.2%
72
 
0.2%
01
 
0.1%
51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1281
11.9%
L966
 
8.9%
A860
 
8.0%
E846
 
7.8%
R740
 
6.9%
N694
 
6.4%
I669
 
6.2%
T643
 
6.0%
C624
 
5.8%
O545
 
5.0%
Other values (27)2927
27.1%

OTHER NAMES(5)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct461
Distinct (%)95.4%
Missing313805
Missing (%)99.8%
Memory size2.4 MiB
BANK OF UTAH TRUSTEE
 
4
WB STUDIO ENTERPRISES INC
 
4
WILMINGTON TRUST CO TRUSTEE
 
4
WELLS FARGO BANK NORTHWEST NA TRUSTEE
 
3
NELSON BROTHERS INC
 
3
Other values (456)
465 

Length

Max length47
Median length31
Mean length18.04554865
Min length6

Characters and Unicode

Total characters8716
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique447 ?
Unique (%)92.5%

Sample

1st rowDLF AIRCRAFT LLC
2nd rowWINDSONG III LLC
3rd rowSALESFORCE.COM INC
4th rowBURR OAK AIR LLC
5th rowHANESBRANDS INC

Common Values

ValueCountFrequency (%)
BANK OF UTAH TRUSTEE4
 
< 0.1%
WB STUDIO ENTERPRISES INC4
 
< 0.1%
WILMINGTON TRUST CO TRUSTEE4
 
< 0.1%
WELLS FARGO BANK NORTHWEST NA TRUSTEE3
 
< 0.1%
NELSON BROTHERS INC3
 
< 0.1%
HANOCO AIR LLC2
 
< 0.1%
LEXINGTON PARTNERS INC2
 
< 0.1%
NATIONAL LIABILITY & FIRE INSURANCE CO2
 
< 0.1%
HOLOGIC INC2
 
< 0.1%
DICKINSON WILLIAM2
 
< 0.1%
Other values (451)455
 
0.1%
(Missing)313805
99.8%

Length

2022-06-07T23:25:46.878670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llc211
 
14.0%
inc64
 
4.2%
aviation31
 
2.1%
trustee30
 
2.0%
co28
 
1.9%
air27
 
1.8%
corp22
 
1.5%
management18
 
1.2%
holdings16
 
1.1%
j16
 
1.1%
Other values (752)1045
69.3%

Most occurring characters

ValueCountFrequency (%)
1025
11.8%
L788
 
9.0%
E752
 
8.6%
A663
 
7.6%
R606
 
7.0%
N570
 
6.5%
I564
 
6.5%
C538
 
6.2%
T469
 
5.4%
S460
 
5.3%
Other values (34)2281
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7638
87.6%
Space Separator1025
 
11.8%
Decimal Number28
 
0.3%
Other Punctuation14
 
0.2%
Dash Punctuation9
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L788
10.3%
E752
9.8%
A663
 
8.7%
R606
 
7.9%
N570
 
7.5%
I564
 
7.4%
C538
 
7.0%
T469
 
6.1%
S460
 
6.0%
O427
 
5.6%
Other values (16)1801
23.6%
Decimal Number
ValueCountFrequency (%)
08
28.6%
15
17.9%
43
 
10.7%
83
 
10.7%
32
 
7.1%
52
 
7.1%
22
 
7.1%
92
 
7.1%
61
 
3.6%
Other Punctuation
ValueCountFrequency (%)
&7
50.0%
/3
21.4%
'2
 
14.3%
.1
 
7.1%
!1
 
7.1%
Space Separator
ValueCountFrequency (%)
1025
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7638
87.6%
Common1078
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
L788
10.3%
E752
9.8%
A663
 
8.7%
R606
 
7.9%
N570
 
7.5%
I564
 
7.4%
C538
 
7.0%
T469
 
6.1%
S460
 
6.0%
O427
 
5.6%
Other values (16)1801
23.6%
Common
ValueCountFrequency (%)
1025
95.1%
-9
 
0.8%
08
 
0.7%
&7
 
0.6%
15
 
0.5%
43
 
0.3%
/3
 
0.3%
83
 
0.3%
32
 
0.2%
'2
 
0.2%
Other values (8)11
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1025
11.8%
L788
 
9.0%
E752
 
8.6%
A663
 
7.6%
R606
 
7.0%
N570
 
6.5%
I564
 
6.5%
C538
 
6.2%
T469
 
5.4%
S460
 
5.3%
Other values (34)2281
26.2%

EXPIRATION DATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct97
Distinct (%)< 0.1%
Missing14297
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean20187390.75
Minimum19710618
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:47.034546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19710618
5-th percentile20170228
Q120180630
median20190430
Q320200229
95-th percentile20200930
Maximum20201231
Range490613
Interquartile range (IQR)19599

Descriptive statistics

Standard deviation13384.11675
Coefficient of variation (CV)0.0006629938912
Kurtosis26.0519469
Mean20187390.75
Median Absolute Deviation (MAD)9799
Skewness-2.33004112
Sum6.056035539 × 1012
Variance179134581.1
MonotonicityNot monotonic
2022-06-07T23:25:47.350644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020043012719
 
4.0%
2020013112421
 
4.0%
2018043011272
 
3.6%
2018013110724
 
3.4%
2020073110690
 
3.4%
201907319920
 
3.2%
201807319825
 
3.1%
201910319720
 
3.1%
201810319568
 
3.0%
202005319471
 
3.0%
Other values (87)193661
61.6%
(Missing)14297
 
4.5%
ValueCountFrequency (%)
197106182
 
< 0.1%
197106211
 
< 0.1%
197109031
 
< 0.1%
201209306
 
< 0.1%
20121231388
 
0.1%
20130331847
0.3%
20130630822
0.3%
20130930945
0.3%
2013113064
 
< 0.1%
201312311088
0.3%
ValueCountFrequency (%)
202012311526
 
0.5%
202011302870
 
0.9%
202010316595
2.1%
202009304096
 
1.3%
202008315985
1.9%
2020073110690
3.4%
202006307868
2.5%
202005319471
3.0%
2020043012719
4.0%
202004291
 
< 0.1%

UNIQUE ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct314288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543833.3043
Minimum0
Maximum1259473
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-06-07T23:25:47.537931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46392.15
Q1241189.75
median491310.5
Q3835633.25
95-th percentile1173010.3
Maximum1259473
Range1259473
Interquartile range (IQR)594443.5

Descriptive statistics

Standard deviation354371.4843
Coefficient of variation (CV)0.6516178423
Kurtosis-1.076032544
Mean543833.3043
Median Absolute Deviation (MAD)290200
Skewness0.2965211819
Sum1.709202815 × 1011
Variance1.255791489 × 1011
MonotonicityNot monotonic
2022-06-07T23:25:47.873794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5241011
 
< 0.1%
1001271
 
< 0.1%
4201271
 
< 0.1%
4301271
 
< 0.1%
4501271
 
< 0.1%
4601271
 
< 0.1%
4701271
 
< 0.1%
4801271
 
< 0.1%
801271
 
< 0.1%
2507181
 
< 0.1%
Other values (314278)314278
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
121
< 0.1%
131
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
12594731
< 0.1%
12594701
< 0.1%
12594661
< 0.1%
12594571
< 0.1%
12594561
< 0.1%
12594551
< 0.1%
12594541
< 0.1%
12594531
< 0.1%
12594521
< 0.1%
12594511
< 0.1%

KIT MFR
Categorical

HIGH CARDINALITY
MISSING

Distinct481
Distinct (%)3.5%
Missing300388
Missing (%)95.6%
Memory size2.4 MiB
VANS AIRCRAFT INC
5383 
LANCAIR INTL INC
 
587
ZENITH AIRCRAFT CO
 
498
ZENITH ACFT CO
 
466
RANS INC
 
455
Other values (476)
6511 

Length

Max length30
Median length29
Mean length17.42251799
Min length3

Characters and Unicode

Total characters242173
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)1.3%

Sample

1st rowQUICKSILVER MANUFACTURING INC
2nd rowVANS AIRCRAFT INC
3rd rowLANCAIR INTL INC
4th rowRANS INC
5th rowRANS INC

Common Values

ValueCountFrequency (%)
VANS AIRCRAFT INC5383
 
1.7%
LANCAIR INTL INC587
 
0.2%
ZENITH AIRCRAFT CO498
 
0.2%
ZENITH ACFT CO466
 
0.1%
RANS INC455
 
0.1%
QUAD CITY ULTRALIGHT ACFT CORP425
 
0.1%
SONEX AIRCRAFT LLC363
 
0.1%
STODDARD HAMILTON355
 
0.1%
SKYSTAR ACFT CO285
 
0.1%
JUST AIRCRAFT LLC251
 
0.1%
Other values (471)4832
 
1.5%
(Missing)300388
95.6%

Length

2022-06-07T23:25:48.030267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc7832
19.0%
aircraft7734
18.7%
vans5386
13.0%
co1483
 
3.6%
llc1426
 
3.5%
acft1288
 
3.1%
zenith977
 
2.4%
intl845
 
2.0%
lancair644
 
1.6%
corp640
 
1.6%
Other values (515)13020
31.5%

Most occurring characters

ValueCountFrequency (%)
A31770
13.1%
27381
11.3%
I24056
9.9%
R24049
9.9%
C23633
9.8%
N20771
8.6%
T17640
7.3%
S10776
 
4.4%
F9904
 
4.1%
O8330
 
3.4%
Other values (26)43863
18.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter214498
88.6%
Space Separator27381
 
11.3%
Other Punctuation118
 
< 0.1%
Dash Punctuation102
 
< 0.1%
Decimal Number58
 
< 0.1%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A31770
14.8%
I24056
11.2%
R24049
11.2%
C23633
11.0%
N20771
9.7%
T17640
8.2%
S10776
 
5.0%
F9904
 
4.6%
O8330
 
3.9%
L8061
 
3.8%
Other values (16)35508
16.6%
Other Punctuation
ValueCountFrequency (%)
&102
86.4%
/14
 
11.9%
'2
 
1.7%
Decimal Number
ValueCountFrequency (%)
528
48.3%
128
48.3%
22
 
3.4%
Space Separator
ValueCountFrequency (%)
27381
100.0%
Dash Punctuation
ValueCountFrequency (%)
-102
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin214498
88.6%
Common27675
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A31770
14.8%
I24056
11.2%
R24049
11.2%
C23633
11.0%
N20771
9.7%
T17640
8.2%
S10776
 
5.0%
F9904
 
4.6%
O8330
 
3.9%
L8061
 
3.8%
Other values (16)35508
16.6%
Common
ValueCountFrequency (%)
27381
98.9%
-102
 
0.4%
&102
 
0.4%
528
 
0.1%
128
 
0.1%
/14
 
0.1%
(8
 
< 0.1%
)8
 
< 0.1%
22
 
< 0.1%
'2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII242173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A31770
13.1%
27381
11.3%
I24056
9.9%
R24049
9.9%
C23633
9.8%
N20771
8.6%
T17640
7.3%
S10776
 
4.4%
F9904
 
4.1%
O8330
 
3.4%
Other values (26)43863
18.1%

KIT MODEL
Categorical

HIGH CARDINALITY
MISSING

Distinct956
Distinct (%)6.9%
Missing300388
Missing (%)95.6%
Memory size2.4 MiB
RV-8
 
996
RV7A
 
758
RV-9A
 
620
RV-6A
 
601
RV-7
 
577
Other values (951)
10348 

Length

Max length20
Median length18
Mean length8.155179856
Min length2

Characters and Unicode

Total characters113357
Distinct characters44
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique335 ?
Unique (%)2.4%

Sample

1st rowSPORT 2S
2nd rowRV-6
3rd rowLANCAIR IV P
4th rowS-9 CHAOS
5th rowS-12 XL AIRAILE

Common Values

ValueCountFrequency (%)
RV-8996
 
0.3%
RV7A758
 
0.2%
RV-9A620
 
0.2%
RV-6A601
 
0.2%
RV-7577
 
0.2%
RV-6513
 
0.2%
RV-10450
 
0.1%
CHALLENGER II404
 
0.1%
RV-4325
 
0.1%
SONEX306
 
0.1%
Other values (946)8350
 
2.7%
(Missing)300388
95.6%

Length

2022-06-07T23:25:48.171529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rv-81002
 
4.5%
ii927
 
4.2%
rv7a758
 
3.4%
rv-9a620
 
2.8%
ch617
 
2.8%
rv-6a601
 
2.7%
rv-7577
 
2.6%
rv-6517
 
2.3%
stol463
 
2.1%
rv-10450
 
2.0%
Other values (907)15764
70.7%

Most occurring characters

ValueCountFrequency (%)
R11053
 
9.8%
A9043
 
8.0%
8396
 
7.4%
I7004
 
6.2%
V6329
 
5.6%
S6124
 
5.4%
-6106
 
5.4%
E6095
 
5.4%
L4785
 
4.2%
O4098
 
3.6%
Other values (34)44324
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter83764
73.9%
Decimal Number14872
 
13.1%
Space Separator8396
 
7.4%
Dash Punctuation6106
 
5.4%
Other Punctuation162
 
0.1%
Open Punctuation23
 
< 0.1%
Close Punctuation23
 
< 0.1%
Math Symbol11
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R11053
13.2%
A9043
10.8%
I7004
 
8.4%
V6329
 
7.6%
S6124
 
7.3%
E6095
 
7.3%
L4785
 
5.7%
O4098
 
4.9%
T4093
 
4.9%
C4051
 
4.8%
Other values (16)21089
25.2%
Decimal Number
ValueCountFrequency (%)
02741
18.4%
12421
16.3%
72256
15.2%
62197
14.8%
81591
10.7%
21235
8.3%
9799
 
5.4%
5678
 
4.6%
4516
 
3.5%
3438
 
2.9%
Other Punctuation
ValueCountFrequency (%)
/153
94.4%
.7
 
4.3%
"2
 
1.2%
Space Separator
ValueCountFrequency (%)
8396
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6106
100.0%
Open Punctuation
ValueCountFrequency (%)
(23
100.0%
Close Punctuation
ValueCountFrequency (%)
)23
100.0%
Math Symbol
ValueCountFrequency (%)
+11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin83764
73.9%
Common29593
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R11053
13.2%
A9043
10.8%
I7004
 
8.4%
V6329
 
7.6%
S6124
 
7.3%
E6095
 
7.3%
L4785
 
5.7%
O4098
 
4.9%
T4093
 
4.9%
C4051
 
4.8%
Other values (16)21089
25.2%
Common
ValueCountFrequency (%)
8396
28.4%
-6106
20.6%
02741
 
9.3%
12421
 
8.2%
72256
 
7.6%
62197
 
7.4%
81591
 
5.4%
21235
 
4.2%
9799
 
2.7%
5678
 
2.3%
Other values (8)1173
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R11053
 
9.8%
A9043
 
8.0%
8396
 
7.4%
I7004
 
6.2%
V6329
 
5.6%
S6124
 
5.4%
-6106
 
5.4%
E6095
 
5.4%
L4785
 
4.2%
O4098
 
3.6%
Other values (34)44324
39.1%

MODE S CODE HEX
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct314288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
A00001
 
1
A91936
 
1
A91925
 
1
A91923
 
1
A91920
 
1
Other values (314283)
314283 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1885728
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314288 ?
Unique (%)100.0%

Sample

1st rowA00001
2nd rowA004B3
3rd rowA00726
4th rowA00727
5th rowA00728

Common Values

ValueCountFrequency (%)
A000011
 
< 0.1%
A919361
 
< 0.1%
A919251
 
< 0.1%
A919231
 
< 0.1%
A919201
 
< 0.1%
A9191F1
 
< 0.1%
A9191E1
 
< 0.1%
A9191B1
 
< 0.1%
A919391
 
< 0.1%
A919351
 
< 0.1%
Other values (314278)314278
> 99.9%

Length

2022-06-07T23:25:48.300204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a000011
 
< 0.1%
a007121
 
< 0.1%
a007291
 
< 0.1%
a0072b1
 
< 0.1%
a0072c1
 
< 0.1%
a0072d1
 
< 0.1%
a0072e1
 
< 0.1%
a0070d1
 
< 0.1%
a007111
 
< 0.1%
a007141
 
< 0.1%
Other values (314278)314278
> 99.9%

Most occurring characters

ValueCountFrequency (%)
A415494
22.0%
0106851
 
5.7%
1105600
 
5.6%
3104000
 
5.5%
9102104
 
5.4%
2101939
 
5.4%
4101198
 
5.4%
6100861
 
5.3%
5100477
 
5.3%
799105
 
5.3%
Other values (6)548099
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1020810
54.1%
Uppercase Letter864918
45.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0106851
10.5%
1105600
10.3%
3104000
10.2%
9102104
10.0%
2101939
10.0%
4101198
9.9%
6100861
9.9%
5100477
9.8%
799105
9.7%
898675
9.7%
Uppercase Letter
ValueCountFrequency (%)
A415494
48.0%
C98832
 
11.4%
B98405
 
11.4%
D96580
 
11.2%
E78766
 
9.1%
F76841
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common1020810
54.1%
Latin864918
45.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0106851
10.5%
1105600
10.3%
3104000
10.2%
9102104
10.0%
2101939
10.0%
4101198
9.9%
6100861
9.9%
5100477
9.8%
799105
9.7%
898675
9.7%
Latin
ValueCountFrequency (%)
A415494
48.0%
C98832
 
11.4%
B98405
 
11.4%
D96580
 
11.2%
E78766
 
9.1%
F76841
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1885728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A415494
22.0%
0106851
 
5.7%
1105600
 
5.6%
3104000
 
5.5%
9102104
 
5.4%
2101939
 
5.4%
4101198
 
5.4%
6100861
 
5.3%
5100477
 
5.3%
799105
 
5.3%
Other values (6)548099
29.1%

X35
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing314288
Missing (%)100.0%
Memory size2.4 MiB

Interactions

2022-06-07T23:25:27.104820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:47.151581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:51.905586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:57.635844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:00.351313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:03.125128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:05.623992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:08.576414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:10.995531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:13.970442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:19.855663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:24.237886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:27.305366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:47.350553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:52.159532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:57.959967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:00.525712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:03.314033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:05.827193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:08.758946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:11.202038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:14.169253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:20.155649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:24.459509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:27.561712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:47.542097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:52.427269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:58.234757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:00.734323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:03.514259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:06.074089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:08.960698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:11.417990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:14.386162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:20.798580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:24.720045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:27.761978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:47.740051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:52.735892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:58.458992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:00.949225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:03.700305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:06.284462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:09.152847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:11.651656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:14.598194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:21.223209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:24.994870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:27.962989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:47.908985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:53.005662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:58.649639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:01.323760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:03.872806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:06.503693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:09.357784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:11.879057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:14.804427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:21.631516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:25.269958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:28.169301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:48.622730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:53.301337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:58.836519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:01.503548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:04.105043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:06.708502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:09.548558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:12.093759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:15.382713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:21.915040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:25.502416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:28.397598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:49.279761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:53.629025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:59.066674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:01.699642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:04.300724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:06.935112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:09.767279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:12.303518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:15.971233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:22.426560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:25.724732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:28.674013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:49.997329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:53.928124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:59.289550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:01.884082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:04.561057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:07.247110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:09.965428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:12.496038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:16.618079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:22.708115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:25.936077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:28.882218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:50.562687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:54.213613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:59.533313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:02.063757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:04.748536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:07.532172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:10.164507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:12.937045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:17.282009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:22.965479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:26.176622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:29.073713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:50.886408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:54.539633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:59.758941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:02.263156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:04.942911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:07.762587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:10.352599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:13.226284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:17.663571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:23.334553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:26.406368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:29.282436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:51.340294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:54.868128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:59.947633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:02.453165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:05.141718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:07.975713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:10.548096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:13.435385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:18.347910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:23.597548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:26.654801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:29.535958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:51.617875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:24:56.105592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:00.141548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:02.648602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:05.390596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:08.220177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:10.764306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:13.645979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:19.066301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:23.863430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-07T23:25:26.885913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-07T23:25:48.419454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-07T23:25:48.641377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-07T23:25:48.878650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-07T23:25:49.155243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-07T23:25:49.468020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-07T23:25:30.238109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-07T23:25:32.090078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-07T23:25:35.749863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-07T23:25:36.808116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

N-NUMBERSERIAL NUMBERMFR MDL CODEENG MFR MDLYEAR MFRTYPE REGISTRANTNAMESTREETSTREET2CITYSTATEZIP CODEREGIONCOUNTYCOUNTRYLAST ACTION DATECERT ISSUE DATECERTIFICATIONTYPE AIRCRAFTTYPE ENGINESTATUS CODEMODE S CODEFRACT OWNERAIR WORTH DATEOTHER NAMES(1)OTHER NAMES(2)OTHER NAMES(3)OTHER NAMES(4)OTHER NAMES(5)EXPIRATION DATEUNIQUE IDKIT MFRKIT MODELMODE S CODE HEXX35
011071398011554556.01988.05.0FEDERAL AVIATION ADMINISTRATIONWASHINGTON REAGAN NATIONAL ARPT3201 THOMAS AVE HANGAR 6WASHINGTONDC2000111.0US2016061419900214.01T55V50000001NaN19880909.0NaNNaNNaNNaNNaN20191130.0524101NaNNaNA00001NaN
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Last rows

N-NUMBERSERIAL NUMBERMFR MDL CODEENG MFR MDLYEAR MFRTYPE REGISTRANTNAMESTREETSTREET2CITYSTATEZIP CODEREGIONCOUNTYCOUNTRYLAST ACTION DATECERT ISSUE DATECERTIFICATIONTYPE AIRCRAFTTYPE ENGINESTATUS CODEMODE S CODEFRACT OWNERAIR WORTH DATEOTHER NAMES(1)OTHER NAMES(2)OTHER NAMES(3)OTHER NAMES(4)OTHER NAMES(5)EXPIRATION DATEUNIQUE IDKIT MFRKIT MODELMODE S CODE HEXX35
3142789ZFZ610204TUEXP059001855572.02005.01.0MOHAMMED QAISS1 GOLDFINCH CIRNaNPHOENIXVILLEPA194601001129.0US2017070320170703.048A48V53066277NaN20060920.0NaNNaNNaNNaNNaN20200731.0162099NaNNaNAC6CBFNaN
3142799ZJ1582868050260065.01961.03.0JAMES R POPE LLCPO BOX 54NaNCLARKSTONWA994030054S3.0US2016100520110228.03163V53066302NaN20010925.0NaNNaNNaNNaNNaN20200229.0334044NaNNaNAC6CC2NaN
3142809ZPL2K-106056115U17042.02004.04.0ROTH VICKIPO BOX 421NaNBRISTOWOK740100421237.0US2017020120070412.04241V53066307NaN20040219.0ROTH EDWARD GNaNNaNNaNNaN20200531.0383857LANCAIR INTL INCLANCAIR LEGACY 2000AC6CC7NaN
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3142829ZR2228868051160020.01971.03.0DENALI HELICOPTERS LLCPO BOX 846NaNTALKEETNAAK9967608465170.0US2015013020120628.0163V53066311NaN19931021.0NaNNaNNaNNaNNaN20180630.0183334NaNNaNAC6CC9NaN
3142839ZS2000457601020.01974.01.0COLLINS BRIAN D42 BOGART DRNaNPETERSBURGWV268478166123.0US2016111819960923.01G10V53066312NaN19760312.0NaNNaNNaNNaNNaN20180831.0221481NaNNaNAC6CCANaN
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3142869ZV379311815113020.01984.03.0L & R INVESTMENT PARTNERS LLC160 GREENTREE DR STE 101NaNDOVERDE19904762011.0US2016090620160906.01N63V53066315NaN20010710.0NaNNaNNaNNaNNaN20190930.0281264NaNNaNAC6CCDNaN
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